diff --git a/.gitignore b/.gitignore index 8b262b0..1dbda65 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,8 @@ __pycache__/ logs/ working/ +results/ +.mypy_cache/ .env .DS_Store .vscode/ @@ -18,4 +20,7 @@ test_*.py .planning/ .ruff_cache/ Start_MCP_Server.bat +.deepeval/ +.pytest_cache/ + diff --git a/ddls_extracted/evals_employees_DDL.sql b/ddls_extracted/evals_employees_DDL.sql deleted file mode 100644 index 935ffe2..0000000 --- a/ddls_extracted/evals_employees_DDL.sql +++ /dev/null @@ -1,25 +0,0 @@ -/* - * File: evals_employees_DDL.sql - * Generated: 2026-06-23 14:16:55 - * Type: TABLE - * Database: demo_user - * Object: evals_employees - * Size: 572 characters - */ - -CREATE SET TABLE demo_user.evals_employees ,FALLBACK , - NO BEFORE JOURNAL, - NO AFTER JOURNAL, - CHECKSUM = DEFAULT, - DEFAULT MERGEBLOCKRATIO, - MAP = TD_MAP1 - ( - employee_id INTEGER NOT NULL, - name VARCHAR(100) CHARACTER SET LATIN NOT CASESPECIFIC NOT NULL, - department VARCHAR(50) CHARACTER SET LATIN NOT CASESPECIFIC NOT NULL, - salary DECIMAL(10,2), - region VARCHAR(50) CHARACTER SET LATIN NOT CASESPECIFIC NOT NULL, - hire_date DATE FORMAT 'YY/MM/DD' NOT NULL, - manager_id INTEGER, -PRIMARY KEY ( employee_id )) -; \ No newline at end of file diff --git a/ddls_extracted/evals_orders_DDL.sql b/ddls_extracted/evals_orders_DDL.sql deleted file mode 100644 index 8c90da9..0000000 --- a/ddls_extracted/evals_orders_DDL.sql +++ /dev/null @@ -1,25 +0,0 @@ -/* - * File: evals_orders_DDL.sql - * Generated: 2026-06-23 14:16:30 - * Type: TABLE - * Database: demo_user - * Object: evals_orders - * Size: 563 characters - */ - -CREATE SET TABLE demo_user.evals_orders ,FALLBACK , - NO BEFORE JOURNAL, - NO AFTER JOURNAL, - CHECKSUM = DEFAULT, - DEFAULT MERGEBLOCKRATIO, - MAP = TD_MAP1 - ( - order_id INTEGER NOT NULL, - customer_name VARCHAR(100) CHARACTER SET LATIN NOT CASESPECIFIC NOT NULL, - order_date DATE FORMAT 'YY/MM/DD' NOT NULL, - ship_date DATE FORMAT 'YY/MM/DD', - amount DECIMAL(10,2) NOT NULL, - product_category VARCHAR(50) CHARACTER SET LATIN NOT CASESPECIFIC NOT NULL, - quantity INTEGER NOT NULL, -PRIMARY KEY ( order_id )) -; \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index ca299dd..9a45208 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,6 +24,7 @@ classifiers = [ ] dependencies = [ "pydantic>=2.8.0,<3.0.0", + "pydantic-settings>=2.14.2", "fastmcp>=3.2.0,<3.4.2", "mcp[cli]>=1.0.0,<2.0.0", "teradatasqlalchemy>=20.0.0.0", @@ -31,6 +32,7 @@ dependencies = [ "PyYAML>=6.0.0", "sqlalchemy>=2.0.0,<3.0.0", "starlette>=1.3.1", + "langsmith>=0.8.18", ] [project.optional-dependencies] diff --git a/scripts/seed_tdml_summaries.py b/scripts/seed_tdml_summaries.py deleted file mode 100644 index 20bc392..0000000 --- a/scripts/seed_tdml_summaries.py +++ /dev/null @@ -1,198 +0,0 @@ -""" -Seed script: connects to Teradata, extracts one-line summaries from teradataml -__init__.__doc__ for all TD_ANALYTIC_FUNCS, then prints the new dict[str, str] -block ready to paste into constants.py. - -Requires DATABASE_URI env var: - export DATABASE_URI="teradata://user:pass@host:1025/db" - uv run python scripts/seed_tdml_summaries.py -""" - -import os -import re -import textwrap -import warnings - -warnings.filterwarnings("ignore") - -import teradataml as tdml # noqa: E402 - -# Connect so that teradataml populates __init__.__doc__ on each class -_uri = os.environ.get("DATABASE_URI", "") -if _uri: - _m = re.match(r"teradata://([^:]+):([^@]+)@([^:]+):(\d+)/(.+)", _uri) - if _m: - tdml.create_context( - host=_m.group(3), - username=_m.group(1), - password=_m.group(2), - database=_m.group(5), - ) - else: - raise ValueError(f"Cannot parse DATABASE_URI: {_uri}") -else: - raise EnvironmentError("DATABASE_URI not set — docstrings require a live connection") - -FUNCS = [ - "ANOVA", - "Attribution", - "Antiselect", - "Apriori", - "BincodeFit", - "BincodeTransform", - "CFilter", - "CategoricalSummary", - "ChiSq", - "ClassificationEvaluator", - "ColumnSummary", - "ColumnTransformer", - "ConvertTo", - "DecisionForest", - "FTest", - "FillRowId", - "Fit", - "GetFutileColumns", - "GetRowsWithMissingValues", - "GetRowsWithoutMissingValues", - "GLM", - "GLMPerSegment", - "Histogram", - "KMeans", - "KMeansPredict", - "KNN", - "MovingAverage", - "NERExtractor", - "NGramSplitter", - "NaiveBayesTextClassifierPredict", - "NaiveBayesTextClassifierTrainer", - "NonLinearCombineFit", - "NonLinearCombineTransform", - "NumApply", - "NPath", - "OneClassSVM", - "OneClassSVMPredict", - "OneHotEncodingFit", - "OneHotEncodingTransform", - "OrdinalEncodingFit", - "OrdinalEncodingTransform", - "OutlierFilterFit", - "OutlierFilterTransform", - "Pack", - "PolynomialFeaturesFit", - "PolynomialFeaturesTransform", - "Pivoting", - "QQNorm", - "ROC", - "RandomProjectionFit", - "RandomProjectionMinComponents", - "RandomProjectionTransform", - "RegressionEvaluator", - "RoundColumns", - "RowNormalizeFit", - "RowNormalizeTransform", - "SMOTE", - "SVM", - "SVMPredict", - "ScaleFit", - "ScaleTransform", - "Sessionize", - "SentimentExtractor", - "Shap", - "Silhouette", - "SimpleImputeFit", - "SimpleImputeTransform", - "StrApply", - "StringSimilarity", - "TDDecisionForestPredict", - "TDGLMPredict", - "TDNaiveBayesPredict", - "TFIDF", - "TargetEncodingFit", - "TargetEncodingTransform", - "TextMorph", - "TextParser", - "TrainTestSplit", - "Transform", - "UnivariateStatistics", - "Unpack", - "Unpivoting", - "VectorDistance", - "WhichMax", - "WhichMin", - "WordEmbeddings", - "XGBoost", - "XGBoostPredict", - "ZTest", -] - - -def extract_summary(func_name: str) -> str: - """Pull the first meaningful sentence from the teradataml __init__ docstring.""" - func_obj = getattr(tdml, func_name, None) - if func_obj is None: - return f"Teradata ML analytic function {func_name}." - - raw = getattr(func_obj.__init__, "__doc__", None) or "" - # Dedent and strip leading blank lines - raw = textwrap.dedent(raw).strip() - - # The teradataml pattern is: - # DESCRIPTION: - # - # - # PARAMETERS: - # Try to grab the DESCRIPTION block first. - desc_match = re.search(r"DESCRIPTION\s*:\s*\n(.*?)(?:\n\s*\n|\n\s*PARAMETERS\s*:)", raw, re.DOTALL) - if desc_match: - block = desc_match.group(1) - else: - # Fallback: take the first non-empty paragraph - block = raw.split("\n\n")[0] - - # Collapse internal whitespace / newlines into a single line - block = re.sub(r"\s+", " ", block).strip() - - # Replace teradataml-specific terminology - block = block.replace("teradataml DataFrame", "table name") - block = block.replace("DataFrame", "table name") - - # Truncate at the first sentence boundary (period followed by space or end) - # Keep the trailing period. - sent_match = re.search(r"^(.*?\.)\s", block) - if sent_match: - summary = sent_match.group(1) - else: - # No sentence boundary — use the whole block but cap length - summary = block[:200].rstrip() - if not summary.endswith("."): - summary += "." - - return summary - - -def main(): - results: list[tuple[str, str]] = [] - missing: list[str] = [] - - for name in FUNCS: - summary = extract_summary(name) - results.append((name, summary)) - if "analytic function" in summary and name in summary: - missing.append(name) - - # Print the dict literal ready to paste into constants.py - print("TD_ANALYTIC_FUNCS = {") - for name, summary in results: - # Escape any quotes inside the summary - safe = summary.replace('"', '\\"') - print(f' "{name}": "{safe}",') - print("}") - - if missing: - print(f"\n# WARNING: {len(missing)} functions had no extractable docstring — fallback used:") - for m in missing: - print(f"# {m}") - - -if __name__ == "__main__": - main() diff --git a/tests/eval_test/.deepeval/.deepeval_telemetry.txt b/tests/eval_test/.deepeval/.deepeval_telemetry.txt new file mode 100644 index 0000000..3d80a6d --- /dev/null +++ b/tests/eval_test/.deepeval/.deepeval_telemetry.txt @@ -0,0 +1,4 @@ +DEEPEVAL_ID=66f13728-e025-470d-b8fc-582a61a9d898 +DEEPEVAL_STATUS=old +DEEPEVAL_LAST_FEATURE=evaluation +DEEPEVAL_EVALUATION_STATUS=old diff --git a/tests/eval_test/.env.example b/tests/eval_test/.env.example new file mode 100644 index 0000000..7a71e4e --- /dev/null +++ b/tests/eval_test/.env.example @@ -0,0 +1,33 @@ +# AWS credentials — boto3 uses the standard credential chain: +# env vars → ~/.aws/credentials → IAM instance profile +AWS_REGION=us-east-1 +AWS_ACCESS_KEY_ID=your-access-key-id +AWS_SECRET_ACCESS_KEY=your-secret-access-key +# AWS_SESSION_TOKEN=your-session-token # only needed for temporary credentials + +# Claude model ID on Bedrock — used for both agent and judge unless overridden +BEDROCK_MODEL_ID=anthropic.claude-3-5-sonnet-20241022-v2:0 + +# Override judge model separately (optional — defaults to BEDROCK_MODEL_ID) +# BEDROCK_JUDGE_MODEL_ID=anthropic.claude-3-5-sonnet-20241022-v2:0 + +# MCP server URL (must be running before evals start) +MCP_SERVER_URL=http://127.0.0.1:8001/mcp + +# Database where evals_employees and evals_orders are created (usually your ClearScape username) +EVALS_DATABASE=your_database_name + +# Teradata connection — used by setup_test_data.py and teardown_test_data.py only +TERADATA_HOST=your-clearscape-host.teradata.com +TERADATA_USER=your_username +TERADATA_PASSWORD=your_password + +# Max tool-use turns per eval case (single-turn cases) +AGENT_MAX_STEPS=5 + +# Max tool-use turns per conversation turn (multi-turn cases) +AGENT_MAX_STEPS_PER_TURN=3 + +# Tool descriptions seen by the agent during evals (default: live MCP server) +# USE_DESCRIPTION_OVERRIDES=1 +# DESCRIPTION_OVERRIDES_FILE=description_overrides.json \ No newline at end of file diff --git a/tests/eval_test/AGENTS.md b/tests/eval_test/AGENTS.md new file mode 100644 index 0000000..85b4481 --- /dev/null +++ b/tests/eval_test/AGENTS.md @@ -0,0 +1,189 @@ +# PROJECT KNOWLEDGE BASE + +**Updated:** 2026-06-23 + +## OVERVIEW + +**Project:** teradata-mcp-evals + +**Purpose:** Eval suite for the [Teradata MCP Server](https://github.com/Teradata/teradata-mcp-server) community edition. Primary goal is **MCP tool description quality** — tests whether an LLM agent selects the right tool and forms valid parameters from natural language. Failures on `ambiguous_selection` cases usually indicate overlapping or unclear tool descriptions in the MCP server. + +**Stack:** +- **Language:** Python ≥ 3.11 +- **Package manager / build tool:** `uv` (recommended) or standard `venv` + `pip` +- **Core dependencies:** + - `deepeval>=1.4.0` + - `mcp>=1.0.0` + - `boto3>=1.34.0` + - `python-dotenv>=1.0.0` + - `pytest>=8.0.0` + - `pytest-asyncio>=0.23.0` + - `teradatasql>=20.0.0` +- **Auxiliary tools:** `uv`, `dotenv`, deepeval CLI, `teradatasql` driver, AWS Bedrock (Claude) for agent + judge LLM calls + +**Case types:** + +| Type | Purpose | +|------|---------| +| `happy_path` | Unambiguous prompt → correct tool + params | +| `ambiguous_selection` | Two tools could apply → tests description distinctness | +| `missing_parameter` | Vague prompt → agent must ask, not hallucinate | +| `multi_tool` | Chained tool calls in order | +| `multi_turn` (optional) | 2–7 turns: clarify first, then correct tool after user supplies info | + +**Module priority:** P0 `base` · P1 `dba`, `sec` · P2 `qlty` · maintained `chat`, `plot`, `tmpl` + +## STRUCTURE + +``` +agent/ + client.py # MCP agent; optional description_overrides (opt-in) +judge/ + bedrock_llm.py # DeepEvalBaseLLM wrapper for Bedrock Converse API + checks.py # Deterministic structural checks (fail before LLM judge) + metrics.py # ToolCorrectnessMetric + Clarification GEval + report.py # Eval summaries → results/latest_summary.* + suggest_overrides.py # LLM draft overrides from failed ambiguous_selection cases +cases/ + .json # Eval case definitions per MCP module prefix +tests/ + conftest.py # Fixtures, {EVALS_DATABASE} substitution, assert_eval_case() + case_runner.py # Single/multi-turn execution and per-turn scoring + test_.py # One pytest file per module (all use assert_eval_case) + test_checks.py # Unit tests for judge/checks.py + test_multi_turn.py # Unit tests for multi-turn schema validation +backup/ # Optional bootstrap/audit scripts — see backup/README.md +run_evals.py # CLI entry point (deepeval + pytest) +suggest_overrides.py # CLI: draft description_overrides from eval failures +setup_test_data.py # Create evals_employees / evals_orders in EVALS_DATABASE +preflight.py # Verify eval tables exist before live eval runs +teardown_test_data.py +docs/ # Extended user documentation +README.md # Quick start and doc index +.env.example # Required environment variables +pyproject.toml # Project metadata, Ruff config, pytest paths +``` + +## COMMANDS + +| Action | Command | +|--------|---------| +| **Install dependencies** (recommended) | `curl -LsSf https://astral.sh/uv/install.sh \| sh && uv venv && uv sync` | +| **Install dependencies** (standard) | `python3 -m venv .venv && source .venv/bin/activate && pip install -e .` | +| **Run test data setup** | `python setup_test_data.py` (or `uv run python setup_test_data.py`) | +| **Verify eval tables** | `python preflight.py` (also runs automatically via `run_evals.py`) | +| **Generate happy-path cases** | `uv run python backup/generate_cases.py` | +| **Regenerate after description change** | `uv run python backup/generate_cases.py --module base --overwrite` | +| **Audit ambiguous pair coverage** | `uv run python backup/audit_cases.py --strict` | +| **Audit against live MCP tools** | `uv run python backup/audit_cases.py --live-mcp --strict` | +| **List eval runs** | `uv run python run_evals.py --list-runs` | +| **Suggest description overrides** | `uv run python suggest_overrides.py` (after baseline eval) | +| **Apply description overrides** | `uv run python suggest_overrides.py --apply` (after reviewing draft) | +| **Test description overrides** | `uv run python run_evals.py --with-description-overrides` | +| **Unit tests (local)** | `uv run pytest tests/test_checks.py tests/test_multi_turn.py tests/test_report.py tests/test_suggest_overrides.py tests/test_description_overrides.py -v` | +| **Run full eval suite** | `python run_evals.py` (or `uv run python run_evals.py`) | +| **Run one module** | `python run_evals.py --module base` | +| **Filter by case type** | `python run_evals.py --module base --type ambiguous_selection` | +| **Run multi-turn cases only** | `python run_evals.py --module base --type multi_turn` | +| **Verbose output** | `python run_evals.py --verbose` | +| **Unit tests (no MCP/Bedrock)** | `uv run pytest tests/test_checks.py tests/test_multi_turn.py tests/test_report.py tests/test_suggest_overrides.py tests/test_description_overrides.py` | +| **Run tests directly (pytest)** | `pytest` (or `uv run pytest`) | +| **Tear down test data** | `python teardown_test_data.py` | +| **Package script** | `run-evals` (installed via `[project.scripts]` in `pyproject.toml`) | + +### `--type` filter keywords (maps to pytest `-k` on case IDs) + +| `--type` | Matches IDs containing | +|----------|------------------------| +| `happy_path` | `happy` | +| `ambiguous_selection` | `ambiguous` | +| `missing_parameter` | `missing` | +| `multi_tool` | `multi_tool` | +| `multi_turn` | `clarify_then_call` | + +## CODING STANDARDS + +- **Language:** Python 3.11+ with full type hints (`typing`, `dataclasses`). +- **Style:** PEP 8; line length 120 (Ruff in `pyproject.toml`). +- **Docstrings:** Triple-quoted module- and function-level docstrings. +- **Error handling:** Exceptions caught and surfaced as readable messages (e.g. tool errors in `agent/client.py`). +- **Configuration:** `python-dotenv`; secrets in `.env` (never commit). Key vars: + - `MCP_SERVER_URL`, `EVALS_DATABASE`, `BEDROCK_MODEL_ID` + - `BEDROCK_JUDGE_MODEL_ID` (optional, defaults to agent model) + - `USE_DESCRIPTION_OVERRIDES` / `DESCRIPTION_OVERRIDES_FILE` (optional — default evals use live MCP descriptions) + - `AGENT_MAX_STEPS` (default 5, single-turn) + - `AGENT_MAX_STEPS_PER_TURN` (default 3, multi-turn) +- **Testing:** Pytest + DeepEval for integration evals; pure unit tests for `judge/checks.py` and schema validation without Bedrock/MCP. + +## WHERE TO LOOK + +| Task | Location | +|------|----------| +| Agent loop / MCP session | `agent/client.py` | +| Deterministic checks | `judge/checks.py` | +| LLM judge metrics | `judge/metrics.py` | +| Run + score any case | `tests/case_runner.py` → `assert_eval_case()` | +| Eval run summaries | `judge/report.py` → `results/runs//` + `results/latest.json` | +| List recent runs | `run_evals.py --list-runs` | +| Override suggestions | `suggest_overrides.py` → run's `suggested_overrides.json` | +| Apply override draft | `suggest_overrides.py --apply` → `description_overrides.json` | +| Apply overrides during evals | `description_overrides.json` + `--with-description-overrides` (`agent/client.py`) | +| Case JSON definitions | `cases/.json` | +| Ambiguous pair registry / live MCP diff | `backup/audit_cases.py` → `AMBIGUOUS_PAIRS`, `--live-mcp` | +| Happy-path generator | `backup/generate_cases.py` | +| Test data setup / preflight | `setup_test_data.py`, `preflight.py` | +| `{EVALS_DATABASE}` substitution | `tests/conftest.py` → `_substitute()` |s +| User-facing docs | `README.md`, `docs/` | + +## EVAL FLOW + +**Description quality loop** (see `docs/workflow.md`): + +1. Baseline: `run_evals.py` — agent uses live MCP descriptions. +2. Summary: `judge/report.py` writes `results/runs//` and updates `results/latest.json`. +3. Draft: `suggest_overrides.py` — LLM rewrites for failed `ambiguous_selection` cases. +4. Test: `suggest_overrides.py --apply`, then re-run with `--with-description-overrides`. +5. Ship: promote to MCP server, re-run baseline. + +**Per-case scoring:** + +1. Test loads case from `cases/.json` via `load_cases()`. +2. `{EVALS_DATABASE}` placeholder substituted at runtime. +3. **Single-turn:** `run_agent()` → deterministic checks → deepeval metrics. +4. **Multi-turn:** `run_agent_turns()` (one MCP session, max 7 turns) → per-turn checks: + - Clarification turns: no tools + Clarification GEval + - Tool turns: deterministic checks + ToolCorrectnessMetric +5. Structural failures in `judge/checks.py` raise `AssertionError` before the judge runs. +6. Outcomes recorded under `results/runs//` via `judge/report.py`. + +## CASE JSON CONVENTIONS + +**Single-turn:** top-level `input`, `type`, `expected_tools`. + +**Multi-turn:** top-level `turns` (2–7 entries), each with `input` and exactly one of: +- `"expect": "clarification"` +- `"expected_tools": [...]` + +Multi-turn case IDs should contain `clarify_then_call` for `--type multi_turn` filtering. + +Param names in `expected_tools` must match live MCP schemas (e.g. `sql` for `base_readQuery`, `user_name` for sec tools). Use `{EVALS_DATABASE}.evals_*` tables for deterministic grounding in base, qlty, plot, and dba cases where applicable. + +## MCP DESCRIPTION CHURN WORKFLOW + +When a tool description changes in the MCP server: + +1. `backup/generate_cases.py --module --overwrite` for happy paths (optional) +2. Manually review `ambiguous_selection` cases in that module +3. `backup/audit_cases.py --module --strict` (optional) +4. Run baseline evals; use `suggest_overrides.py` on failures; test with `--with-description-overrides` +5. Promote accepted wording to the MCP server repo; re-run baseline evals +6. Add new tool pairs to `AMBIGUOUS_PAIRS` in `backup/audit_cases.py` when overlap is discovered + +## NOTES + +- Requires running **Teradata MCP Server** at `MCP_SERVER_URL` and **ClearScape** database `EVALS_DATABASE` with demo tables from `setup_test_data.py`. +- AWS Bedrock credentials via standard boto3 chain. Default model: `anthropic.claude-3-5-sonnet-20241022-v2:0`. +- Eval results stored in `results/` (not committed). Each run gets `results/runs//` with summary, manifest, and optional suggestion draft. `results/latest.json` points at the newest run; `results/index.json` lists recent runs. +- Do not commit `.env` or credentials. +- Hand-author edge cases (`ambiguous_selection`, `missing_parameter`, `multi_tool`, `multi_turn`) after generating happy paths — generator cannot replace human judgement on description boundaries. +- Prompt vocabulary should differ from tool descriptions (same rule enforced in `backup/generate_cases.py` DRAFT_PROMPT). diff --git a/tests/eval_test/README.md b/tests/eval_test/README.md new file mode 100644 index 0000000..a624b7e --- /dev/null +++ b/tests/eval_test/README.md @@ -0,0 +1,39 @@ +# teradata-mcp-evals + +Eval suite for the [Teradata MCP Server](https://github.com/Teradata/teradata-mcp-server) community edition. + +Tests whether an LLM agent selects the right MCP tool and forms valid parameters from natural language. The primary goal is **MCP tool description quality** — routing failures on `ambiguous_selection` cases usually mean overlapping or unclear descriptions. Uses [deepeval](https://github.com/confident-ai/deepeval) and Claude on AWS Bedrock as agent and judge. + +## Quick start + +```bash +uv venv && uv sync +cp .env.example .env # set MCP_SERVER_URL, EVALS_DATABASE, Bedrock credentials + +python setup_test_data.py +python run_evals.py --module base +``` + +Open `results/latest_summary.md` for pass/fail details, or run `python run_evals.py --list-runs` to browse run directories. + +## Workflow + +Baseline evals use **live MCP descriptions**. To iterate on wording before changing the server: + +1. `run_evals.py` — baseline +2. `suggest_overrides.py` — LLM draft for failed cases +3. `suggest_overrides.py --apply` — merge reviewed draft into `description_overrides.json` +4. `run_evals.py --with-description-overrides` — test locally +5. Promote to MCP server → baseline again + +Full diagram, commands, and results files: **[docs/workflow.md](docs/workflow.md)** + +## Documentation + +| Doc | Contents | +|---|---| +| [docs/setup.md](docs/setup.md) | Install, `.env`, test data, unit tests | +| [docs/workflow.md](docs/workflow.md) | Running evals, overrides, results | +| [docs/cases.md](docs/cases.md) | Case types, JSON format, scoring, adding cases | +| [docs/structure.md](docs/structure.md) | Repository layout | +| [backup/README.md](backup/README.md) | Optional case generation and audit scripts | diff --git a/tests/eval_test/agent/__init__.py b/tests/eval_test/agent/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/eval_test/agent/client.py b/tests/eval_test/agent/client.py new file mode 100644 index 0000000..dd982c3 --- /dev/null +++ b/tests/eval_test/agent/client.py @@ -0,0 +1,388 @@ +"""Multi-turn MCP agent that drives tool selection via Bedrock Claude.""" + +from __future__ import annotations + +import asyncio +import json +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import boto3 +from mcp import ClientSession +from mcp.client.streamable_http import streamablehttp_client + +MAX_TOOL_RESULT_CHARS = int(os.environ.get("MAX_TOOL_RESULT_CHARS", "8000")) + +# --------------------------------------------------------------------------- +# Description overrides (opt-in) +# --------------------------------------------------------------------------- +# By default evals use live MCP server tool descriptions (baseline). +# Set USE_DESCRIPTION_OVERRIDES=1 or pass --with-description-overrides to +# run_evals.py to patch descriptions from description_overrides.json before +# routing — useful for testing proposed wording before changing the MCP server. +# --------------------------------------------------------------------------- + +REPO_ROOT = Path(__file__).resolve().parent.parent +DEFAULT_OVERRIDES_FILE = REPO_ROOT / "description_overrides.json" + + +def description_overrides_enabled() -> bool: + """Return True when evals should patch tool descriptions before routing.""" + if os.environ.get("DESCRIPTION_OVERRIDES_FILE"): + return True + return os.environ.get("USE_DESCRIPTION_OVERRIDES", "").lower() in {"1", "true", "yes"} + + +def resolve_description_overrides_file() -> Path | None: + """Return the overrides file path when overrides are enabled.""" + if not description_overrides_enabled(): + return None + + env_path = os.environ.get("DESCRIPTION_OVERRIDES_FILE") + if env_path: + return Path(env_path) + + if DEFAULT_OVERRIDES_FILE.exists(): + return DEFAULT_OVERRIDES_FILE + + cwd_candidate = Path("description_overrides.json") + if cwd_candidate.exists(): + return cwd_candidate + + return DEFAULT_OVERRIDES_FILE + + +def get_description_override_status() -> dict[str, str | int | None]: + """Summarize which tool descriptions the agent sees during evals.""" + if not description_overrides_enabled(): + return {"mode": "mcp_server", "file": None, "tool_count": 0} + + overrides = _load_description_overrides() + overrides_file = resolve_description_overrides_file() + return { + "mode": "overrides", + "file": str(overrides_file) if overrides_file else None, + "tool_count": len(overrides), + } + + +def _load_description_overrides() -> dict[str, str]: + """Return {tool_name: description} from the overrides file, or {} if absent.""" + if not description_overrides_enabled(): + return {} + + overrides_file = resolve_description_overrides_file() + if overrides_file is None or not overrides_file.exists(): + return {} + + try: + data = json.loads(overrides_file.read_text()) + if isinstance(data, dict): + return {k: v for k, v in data.items() if isinstance(v, str)} + except Exception: + pass + return {} + + +def _apply_description_overrides(tools: list, overrides: dict[str, str]) -> list: + """Return a new list with tool descriptions replaced where an override exists.""" + if not overrides: + return tools + + patched = [] + for tool in tools: + name = getattr(tool, "name", None) + if name and name in overrides: + tool = tool.model_copy(update={"description": overrides[name]}) + patched.append(tool) + return patched + + +@dataclass +class ToolCallRecord: + name: str + input_parameters: dict[str, Any] + + +@dataclass +class TurnResult: + tool_calls: list[ToolCallRecord] + final_response: str + + +@dataclass +class AgentResult: + tool_calls: list[ToolCallRecord] + final_response: str + + +def _make_bedrock_client(region: str = "us-east-1"): + return boto3.client("bedrock-runtime", region_name=region) + + +def _run_async(coro): + """Run an async coroutine, handling both raw asyncio and pytest event loop contexts.""" + try: + loop = asyncio.get_running_loop() + except RuntimeError: + # No running loop — safe to use asyncio.run() + return asyncio.run(coro) + + # A loop is already running. This can happen in pytest with event loop plugins. + # Use asyncio.ensure_future and wait for it to complete. + import concurrent.futures + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit(asyncio.run, coro) + return future.result() + + +def _mcp_tool_to_bedrock(tool) -> dict: + return { + "toolSpec": { + "name": tool.name, + "description": tool.description or "", + "inputSchema": {"json": tool.inputSchema}, + } + } + + +def _extract_text(content_blocks: list[dict]) -> str: + parts: list[str] = [] + for block in content_blocks: + if "text" in block: + parts.append(block["text"]) + elif block.get("type") == "text" and block.get("text"): + parts.append(block["text"]) + return "".join(parts) + + +def _iter_tool_uses(content_blocks: list[dict]): + for block in content_blocks: + if "toolUse" in block: + tool_use = block["toolUse"] + yield tool_use["name"], tool_use.get("input", {}), tool_use["toolUseId"] + elif block.get("type") == "toolUse": + yield block["name"], block.get("input", {}), block["toolUseId"] + + +async def _build_tool_results(session: ClientSession, content_blocks: list[dict]) -> tuple[list[dict], list[ToolCallRecord]]: + tool_results: list[dict] = [] + tool_calls: list[ToolCallRecord] = [] + + for tool_name, tool_input, tool_use_id in _iter_tool_uses(content_blocks): + tool_calls.append(ToolCallRecord(name=tool_name, input_parameters=tool_input)) + + try: + mcp_result = await session.call_tool(tool_name, tool_input) + result_text = json.dumps( + [c.model_dump() for c in mcp_result.content], + default=str, + ) + except Exception as exc: + result_text = f"Tool error: {exc}" + + if len(result_text) > MAX_TOOL_RESULT_CHARS: + result_text = result_text[:MAX_TOOL_RESULT_CHARS] + "\n... [truncated]" + + tool_results.append( + { + "toolResult": { + "toolUseId": tool_use_id, + "content": [{"text": result_text}], + } + } + ) + + return tool_results, tool_calls + + +async def _handle_tool_use( + session: ClientSession, + messages: list[dict], + output_message: dict, + *, + record_calls: list[ToolCallRecord], +) -> bool: + """Append tool results to messages. Returns False if tool_use had no usable blocks.""" + tool_results, tool_calls = await _build_tool_results(session, output_message.get("content", [])) + if not tool_results: + return False + + record_calls.extend(tool_calls) + messages.append({"role": "user", "content": tool_results}) + return True + +async def _run_agent_turns_async( + prompts: list[str], + model_id: str, + bedrock_client, + mcp_url: str, + max_steps_per_turn: int, +) -> list[TurnResult]: + """Run a scripted multi-turn conversation in one MCP session.""" + overrides = _load_description_overrides() + async with streamablehttp_client(mcp_url) as (read, write, _): + async with ClientSession(read, write) as session: + await session.initialize() + tools_response = await session.list_tools() + patched_tools = _apply_description_overrides(tools_response.tools, overrides) + bedrock_tools = [_mcp_tool_to_bedrock(t) for t in patched_tools] + + messages: list[dict] = [] + turn_results: list[TurnResult] = [] + + for prompt in prompts: + messages.append({"role": "user", "content": [{"text": prompt}]}) + turn_tool_calls: list[ToolCallRecord] = [] + final_response = "" + + for _ in range(max_steps_per_turn): + response = bedrock_client.converse( + modelId=model_id, + messages=messages, + toolConfig={ + "tools": bedrock_tools, + "toolChoice": {"auto": {}}, + }, + ) + + stop_reason = response["stopReason"] + output_message = response["output"]["message"] + messages.append(output_message) + + if stop_reason == "tool_use": + handled = await _handle_tool_use( + session, + messages, + output_message, + record_calls=turn_tool_calls, + ) + if not handled: + final_response = _extract_text(output_message.get("content", [])) + break + else: + final_response = _extract_text(output_message.get("content", [])) + break + + turn_results.append( + TurnResult(tool_calls=turn_tool_calls, final_response=final_response) + ) + + return turn_results + + +async def _run_agent_async( + prompt: str, + model_id: str, + bedrock_client, + mcp_url: str, + max_steps: int, +) -> AgentResult: + overrides = _load_description_overrides() + async with streamablehttp_client(mcp_url) as (read, write, _): + async with ClientSession(read, write) as session: + await session.initialize() + tools_response = await session.list_tools() + patched_tools = _apply_description_overrides(tools_response.tools, overrides) + bedrock_tools = [_mcp_tool_to_bedrock(t) for t in patched_tools] + + messages: list[dict] = [ + {"role": "user", "content": [{"text": prompt}]} + ] + tool_calls_made: list[ToolCallRecord] = [] + final_response = "" + + for _ in range(max_steps): + response = bedrock_client.converse( + modelId=model_id, + messages=messages, + toolConfig={ + "tools": bedrock_tools, + "toolChoice": {"auto": {}}, + }, + ) + + stop_reason = response["stopReason"] + output_message = response["output"]["message"] + messages.append(output_message) + + if stop_reason == "tool_use": + handled = await _handle_tool_use( + session, + messages, + output_message, + record_calls=tool_calls_made, + ) + if not handled: + final_response = _extract_text(output_message.get("content", [])) + break + else: + final_response = _extract_text(output_message.get("content", [])) + break + + return AgentResult(tool_calls=tool_calls_made, final_response=final_response) + + +def run_agent( + prompt: str, + model_id: str | None = None, + bedrock_client=None, + mcp_url: str | None = None, + max_steps: int | None = None, +) -> AgentResult: + """Synchronous entry point — runs the async agent loop via asyncio.run().""" + resolved_model = model_id or os.environ.get( + "BEDROCK_MODEL_ID", "anthropic.claude-3-5-sonnet-20241022-v2:0" + ) + resolved_url = mcp_url or os.environ.get("MCP_SERVER_URL", "http://127.0.0.1:8001/mcp") + resolved_steps = max_steps or int(os.environ.get("AGENT_MAX_STEPS", "5")) + + if bedrock_client is None: + bedrock_client = _make_bedrock_client( + region=os.environ.get("AWS_REGION", "us-east-1"), + ) + + return _run_async( + _run_agent_async( + prompt=prompt, + model_id=resolved_model, + bedrock_client=bedrock_client, + mcp_url=resolved_url, + max_steps=resolved_steps, + ) + ) + + +def run_agent_turns( + prompts: list[str], + model_id: str | None = None, + bedrock_client=None, + mcp_url: str | None = None, + max_steps_per_turn: int | None = None, +) -> list[TurnResult]: + """Run a shallow multi-turn conversation (separate tool-call budget per turn).""" + if not prompts: + raise ValueError("prompts must contain at least one user message") + + resolved_model = model_id or os.environ.get( + "BEDROCK_MODEL_ID", "anthropic.claude-3-5-sonnet-20241022-v2:0" + ) + resolved_url = mcp_url or os.environ.get("MCP_SERVER_URL", "http://127.0.0.1:8001/mcp") + resolved_steps = max_steps_per_turn or int(os.environ.get("AGENT_MAX_STEPS_PER_TURN", "3")) + + if bedrock_client is None: + bedrock_client = _make_bedrock_client( + region=os.environ.get("AWS_REGION", "us-east-1"), + ) + + return _run_async( + _run_agent_turns_async( + prompts=prompts, + model_id=resolved_model, + bedrock_client=bedrock_client, + mcp_url=resolved_url, + max_steps_per_turn=resolved_steps, + ) + ) diff --git a/tests/eval_test/backup/README.md b/tests/eval_test/backup/README.md new file mode 100644 index 0000000..0b6eebf --- /dev/null +++ b/tests/eval_test/backup/README.md @@ -0,0 +1,122 @@ +# Case generation, audit, and inspection utilities + +These scripts were used to bootstrap and maintain eval cases during initial development. They are **not part of the main eval workflow** — the case JSON under `cases/` is now the source of truth, and CI runs the core unit tests only. + +Use them when you need to draft new `happy_path` cases from live MCP tool descriptions, inspect live tool definitions, or check ambiguous-pair and live-tool coverage before an MCP server release. + +## Files + +| File | Purpose | +|---|---| +| `generate_cases.py` | Connects to the MCP server, reads tool descriptions, and asks Bedrock Claude to draft one `happy_path` case per tool into `cases/.json`. | +| `audit_cases.py` | Offline checks for registered ambiguous tool-pair coverage; optionally diffs case files against a live MCP server's tool list. | +| `inspect_tools.py` | Dumps live MCP tool names, descriptions, and input schemas to `results/live_descriptions_.json`. | +| `test_audit_cases.py` | Unit tests for the offline audit logic (no MCP or Bedrock required). | + +## Prerequisites + +Same as the main project: Python 3.11+, `.env` configured, and for most commands a running MCP server at `MCP_SERVER_URL`. `generate_cases.py` also requires AWS Bedrock access. + +## Generating happy path cases + +The `base`, `sec`, `chat`, and `plot` modules ship with hand-authored cases. For `dba`, `qlty`, and `tmpl`, you may want to generate happy paths from live tool descriptions first: + +```bash +# One module +uv run python backup/generate_cases.py --module dba + +# All in-scope modules +uv run python backup/generate_cases.py +``` + +Results are written to `cases/.json`. After generating, hand-author `ambiguous_selection`, `missing_parameter`, `multi_tool`, and (optionally) `multi_turn` cases — those require human judgement and cannot be generated cleanly from descriptions alone. + +### Generator flags + +```bash +# Preview without writing to disk +uv run python backup/generate_cases.py --dry-run + +# Overwrite existing happy_path cases (e.g. after a description change) +uv run python backup/generate_cases.py --overwrite +``` + +The generator enforces vocabulary **different from tool descriptions** in its draft prompt — the same rule used when hand-authoring edge cases. + +## Inspecting live tool descriptions + +Requires a running MCP server at `MCP_SERVER_URL`: + +```bash +# Default: base module only +uv run python backup/inspect_tools.py + +# One module +uv run python backup/inspect_tools.py --module dba + +# Every module on the server +uv run python backup/inspect_tools.py --all-modules +``` + +Output is written to `results/live_descriptions_.json` — useful when reviewing description wording before editing cases or MCP server tool definitions. + +## Auditing case coverage + +### Offline audit + +Checks registered ambiguous tool pairs in `cases/*.json` for base, dba, sec, and qlty: + +```bash +# Report gaps for priority modules +uv run python backup/audit_cases.py + +# Fail when pairs are missing coverage +uv run python backup/audit_cases.py --strict + +# One module only +uv run python backup/audit_cases.py --module base --strict +``` + +Registered pairs live in `audit_cases.py` (e.g. `base_readQuery` vs `base_tablePreview`). Extend that registry when you identify new description overlap between tools. + +### Live MCP diff (pre-release) + +Requires a running MCP server at `MCP_SERVER_URL`: + +```bash +# Diff live tool list vs cases — missing happy paths and stale tool names +uv run python backup/audit_cases.py --live-mcp + +# Fail on any gap (priority modules require happy_path for every live tool) +uv run python backup/audit_cases.py --live-mcp --strict + +# Live diff only, skip ambiguous pair checks +uv run python backup/audit_cases.py --live-mcp --skip-pairs --module base +``` + +| Check | Offline `--strict` | `--live-mcp --strict` | +|---|---|---| +| Ambiguous pair coverage | ✅ | ✅ (unless `--skip-pairs`) | +| Missing `happy_path` for live tools | — | ✅ (base, dba, sec, qlty) | +| Stale tool names in cases | — | ✅ | +| Non-priority modules (chat, plot) | — | stale names only; missing happy paths noted, not failed | + +## When MCP tool descriptions change + +1. **Regenerate happy paths** for the affected module: + ```bash + uv run python backup/generate_cases.py --module base --overwrite + ``` +2. **Review hand-authored cases** — especially `ambiguous_selection` prompts for that module. +3. **Run the audit** to confirm pair coverage and (optionally) sync with live tools: + ```bash + uv run python backup/audit_cases.py --module base --strict + uv run python backup/audit_cases.py --live-mcp --strict # requires running MCP server + ``` +4. **Run evals** and treat `ambiguous_selection` failures as description feedback. See [docs/workflow.md](../docs/workflow.md) for the baseline → suggest → override → promote loop. + +## Running unit tests + +```bash +uv run pytest backup/test_audit_cases.py -v +``` diff --git a/tests/eval_test/backup/__init__.py b/tests/eval_test/backup/__init__.py new file mode 100644 index 0000000..4d12f1c --- /dev/null +++ b/tests/eval_test/backup/__init__.py @@ -0,0 +1 @@ +"""Archived case generation and audit utilities (not part of the main eval workflow).""" diff --git a/tests/eval_test/backup/audit_cases.py b/tests/eval_test/backup/audit_cases.py new file mode 100644 index 0000000..78ab7e3 --- /dev/null +++ b/tests/eval_test/backup/audit_cases.py @@ -0,0 +1,291 @@ +""" +Audit eval case coverage: ambiguous tool pairs and (optionally) live MCP tool list. + +Usage: + uv run python backup/audit_cases.py # ambiguous pairs, all priority modules + uv run python backup/audit_cases.py --module base # one module + uv run python backup/audit_cases.py --strict # exit 1 on pair gaps (offline, CI-safe) + uv run python backup/audit_cases.py --strict --live-mcp # also diff against running MCP server +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import os +import sys +from pathlib import Path + +from dotenv import load_dotenv + +load_dotenv() + +CASES_DIR = Path(__file__).resolve().parent.parent / "cases" + +IN_SCOPE_MODULES = frozenset({"base", "dba", "sec", "qlty", "chat", "plot", "tmpl"}) +PRIORITY_MODULES = frozenset({"base", "dba", "sec", "qlty"}) +DEFAULT_AUDIT_MODULES = ("base", "dba", "sec", "qlty") + +# Tool pairs whose descriptions should be distinct; each needs ≥1 ambiguous_selection case. +AMBIGUOUS_PAIRS: dict[str, list[tuple[str, str]]] = { + "base": [ + ("base_readQuery", "base_tablePreview"), + ("base_tableDDL", "base_columnMetadata"), + ("base_tableList", "base_databaseList"), + ("base_saveDDL", "base_tableDDL"), + ("base_tableAffinity", "base_tableUsage"), + ("base_columnDescription", "base_columnMetadata"), + ], + "dba": [ + ("dba_tableSpace", "dba_databaseSpace"), + ("dba_databaseSpace", "dba_systemSpace"), + ("dba_tableSqlList", "dba_userSqlList"), + ("dba_tableUsageImpact", "dba_resusageSummary"), + ("dba_tableUsageImpact", "dba_sessionInfo"), + ("dba_userDelay", "dba_flowControl"), + ], + "sec": [ + ("sec_userDbPermissions", "sec_userRoles"), + ("sec_userDbPermissions", "sec_rolePermissions"), + ("sec_userRoles", "sec_rolePermissions"), + ], + "qlty": [ + ("qlty_missingValues", "qlty_rowsWithMissingValues"), + ("qlty_standardDeviation", "qlty_univariateStatistics"), + ("qlty_columnSummary", "qlty_univariateStatistics"), + ], +} + + +def get_module(tool_name: str) -> str | None: + prefix = tool_name.split("_")[0] + return prefix if prefix in IN_SCOPE_MODULES else None + + +def load_cases(module: str) -> list[dict]: + path = CASES_DIR / f"{module}.json" + if not path.exists(): + return [] + data = json.loads(path.read_text()) + return [c for c in data.get("cases", []) if "id" in c] + + +def tools_referenced_in_cases(cases: list[dict]) -> set[str]: + names: set[str] = set() + for case in cases: + for block in case.get("expected_tools", []): + names.add(block["name"]) + for turn in case.get("turns", []): + for block in turn.get("expected_tools", []): + names.add(block["name"]) + return names + + +def happy_path_tools(cases: list[dict]) -> set[str]: + names: set[str] = set() + for case in cases: + if case.get("type") != "happy_path": + continue + for block in case.get("expected_tools", []): + names.add(block["name"]) + return names + + +def covered_ambiguous_pairs(module: str, cases: list[dict]) -> set[frozenset[str]]: + pairs = AMBIGUOUS_PAIRS.get(module, []) + if not pairs: + return set() + + covered: set[frozenset[str]] = set() + for case in cases: + if case.get("type") != "ambiguous_selection": + continue + expected_tools = case.get("expected_tools", []) + if not expected_tools: + continue + winner = expected_tools[0]["name"] + case_id = case.get("id", "") + description = case.get("description", "") + + for tool_a, tool_b in pairs: + pair = frozenset({tool_a, tool_b}) + if winner not in pair: + continue + loser = tool_b if winner == tool_a else tool_a + loser_token = loser.split("_", 1)[-1] + if loser_token in case_id or loser in description: + covered.add(pair) + + return covered + + +def audit_ambiguous_pair_gaps(module: str, cases: list[dict]) -> list[str]: + pairs = AMBIGUOUS_PAIRS.get(module, []) + if not pairs: + return [] + + covered = covered_ambiguous_pairs(module, cases) + gaps: list[str] = [] + happy_tools = happy_path_tools(cases) + + for tool_a, tool_b in pairs: + pair = frozenset({tool_a, tool_b}) + if pair not in covered: + gaps.append(f" missing ambiguous case: {tool_a} vs {tool_b}") + + for tool_a, tool_b in pairs: + for tool in (tool_a, tool_b): + if tool not in happy_tools: + gaps.append(f" missing happy_path for tool involved in pair: {tool}") + + return gaps + + +def audit_live_tool_gaps( + module: str, + live_tools: set[str], + cases: list[dict], + *, + require_happy_path: bool, +) -> list[str]: + gaps: list[str] = [] + happy_tools = happy_path_tools(cases) + referenced = tools_referenced_in_cases(cases) + + if require_happy_path: + for tool in sorted(live_tools): + if tool not in happy_tools: + gaps.append(f" missing happy_path for live tool: {tool}") + + for tool in sorted(referenced): + if tool not in live_tools: + gaps.append(f" stale tool name in cases (not on MCP server): {tool}") + + extra_live = live_tools - referenced + if extra_live and not require_happy_path: + for tool in sorted(extra_live): + if tool not in happy_tools: + gaps.append(f" note: live tool has no cases yet: {tool}") + + return gaps + + +async def fetch_tools_by_module(mcp_url: str) -> dict[str, set[str]]: + from mcp import ClientSession + from mcp.client.streamable_http import streamablehttp_client + + async with streamablehttp_client(mcp_url) as (read, write, _): + async with ClientSession(read, write) as session: + await session.initialize() + result = await session.list_tools() + + by_module: dict[str, set[str]] = {module: set() for module in IN_SCOPE_MODULES} + for tool in result.tools: + module = get_module(tool.name) + if module: + by_module[module].add(tool.name) + return by_module + + +def audit_module( + module: str, + *, + live_tools: set[str] | None = None, + check_pairs: bool = True, + check_live: bool = False, +) -> list[str]: + cases = load_cases(module) + gaps: list[str] = [] + + if check_pairs: + gaps.extend(audit_ambiguous_pair_gaps(module, cases)) + + if check_live and live_tools is not None: + require_happy = module in PRIORITY_MODULES + gaps.extend( + audit_live_tool_gaps( + module, + live_tools, + cases, + require_happy_path=require_happy, + ) + ) + + return gaps + + +def resolve_modules(module: str | None, live_mcp: bool) -> list[str]: + if module: + return [module] + if live_mcp: + return sorted(IN_SCOPE_MODULES) + return list(DEFAULT_AUDIT_MODULES) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Audit eval case coverage") + parser.add_argument("--module", help="Audit one module only") + parser.add_argument( + "--live-mcp", + action="store_true", + help="Diff cases against tools returned by the running MCP server", + ) + parser.add_argument( + "--skip-pairs", + action="store_true", + help="Skip ambiguous pair checks (live MCP diff only)", + ) + parser.add_argument("--strict", action="store_true", help="Exit 1 if any gaps found") + args = parser.parse_args() + + modules = resolve_modules(args.module, args.live_mcp) + live_by_module: dict[str, set[str]] | None = None + + if args.live_mcp: + mcp_url = os.environ.get("MCP_SERVER_URL", "http://127.0.0.1:8001/mcp") + print(f"Connecting to MCP server at {mcp_url} ...") + try: + live_by_module = asyncio.run(fetch_tools_by_module(mcp_url)) + total = sum(len(tools) for tools in live_by_module.values()) + print(f"Found {total} in-scope tools across {len(IN_SCOPE_MODULES)} modules\n") + except Exception as exc: + print(f"ERROR: could not connect to MCP server: {exc}", file=sys.stderr) + sys.exit(1) + + any_gaps = False + check_pairs = not args.skip_pairs + + for module in modules: + live_tools = live_by_module.get(module, set()) if live_by_module else None + gaps = audit_module( + module, + live_tools=live_tools, + check_pairs=check_pairs and module in AMBIGUOUS_PAIRS, + check_live=args.live_mcp, + ) + + print(f"[{module}]") + if not gaps: + if args.live_mcp and live_tools is not None: + print(f" OK — {len(live_tools)} live tool(s), cases in sync") + elif check_pairs and module in AMBIGUOUS_PAIRS: + print(" OK — all registered ambiguous pairs covered") + else: + print(" OK") + else: + any_gaps = True + print(" GAPS:") + for gap in gaps: + print(gap) + print() + + if any_gaps and args.strict: + sys.exit(1) + + if not any_gaps: + print("Audit passed.") + + +if __name__ == "__main__": + main() diff --git a/tests/eval_test/backup/generate_cases.py b/tests/eval_test/backup/generate_cases.py new file mode 100644 index 0000000..97719dc --- /dev/null +++ b/tests/eval_test/backup/generate_cases.py @@ -0,0 +1,175 @@ +""" +Bootstrap generator: connects to the MCP server, reads tool descriptions, +and asks Bedrock Claude to draft one happy_path test case per tool. + +Usage: + uv run python backup/generate_cases.py # all in-scope modules + uv run python backup/generate_cases.py --module base # one module only + uv run python backup/generate_cases.py --dry-run # print without writing +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import os +import sys +from pathlib import Path + +import boto3 +from dotenv import load_dotenv +from mcp import ClientSession +from mcp.client.streamable_http import streamablehttp_client + +load_dotenv() + +CASES_DIR = Path(__file__).resolve().parent.parent / "cases" +IN_SCOPE_MODULES = {"base", "dba", "sec", "qlty", "chat", "plot", "tmpl"} + +DRAFT_PROMPT = """\ +You are building an eval suite for an MCP tool server connected to a Teradata database. + +Below is the definition of one MCP tool: + +Name: {name} +Description: {description} +Input schema: {schema} + +Write ONE natural language prompt that a business user might type to invoke this tool. +Rules: +- Do NOT copy phrases directly from the tool description — use different vocabulary. +- Use concrete but generic values (e.g. a real-sounding database name like "Sales" or "HR"). +- The prompt should be unambiguous: it should clearly call for THIS tool, not a different one. +- Keep it to one or two sentences. + +Also provide the expected parameter values the tool should be called with. +Use realistic placeholder values where the actual value depends on environment. + +Respond with valid JSON only — no markdown, no explanation: +{{ + "input": "", + "expected_params": {{: , ...}} +}} +""" + + +def get_module(tool_name: str) -> str | None: + prefix = tool_name.split("_")[0] + return prefix if prefix in IN_SCOPE_MODULES else None + + +def make_bedrock_client(): + region = os.environ.get("AWS_REGION", "us-east-1") + return boto3.client("bedrock-runtime", region_name=region) + + +def draft_case(tool, bedrock_client, model_id: str) -> dict | None: + prompt = DRAFT_PROMPT.format( + name=tool.name, + description=tool.description or "(no description)", + schema=json.dumps(tool.inputSchema, indent=2), + ) + try: + response = bedrock_client.converse( + modelId=model_id, + messages=[{"role": "user", "content": [{"text": prompt}]}], + ) + text = response["output"]["message"]["content"][0]["text"].strip() + # Extract JSON even if there's surrounding whitespace or markdown fences + start = text.index("{") + end = text.rindex("}") + 1 + parsed = json.loads(text[start:end]) + return { + "id": f"{tool.name}_happy", + "type": "happy_path", + "description": f"Auto-generated happy path for {tool.name}", + "input": parsed["input"], + "expected_tools": [ + {"name": tool.name, "params": parsed.get("expected_params", {})} + ], + } + except Exception as exc: + print(f" WARNING: could not generate case for {tool.name}: {exc}", file=sys.stderr) + return None + + +async def collect_tools(mcp_url: str) -> list: + async with streamablehttp_client(mcp_url) as (read, write, _): + async with ClientSession(read, write) as session: + await session.initialize() + result = await session.list_tools() + return result.tools + + +def load_existing(module: str) -> list[dict]: + path = CASES_DIR / f"{module}.json" + if not path.exists(): + return [] + data = json.loads(path.read_text()) + return [c for c in data.get("cases", []) if "id" in c] + + +def write_cases(module: str, cases: list[dict]) -> None: + path = CASES_DIR / f"{module}.json" + path.write_text(json.dumps({"module": module, "cases": cases}, indent=2) + "\n") + print(f" Wrote {len(cases)} cases → {path}") + + +def main() -> None: + parser = argparse.ArgumentParser(description="Generate happy_path eval cases from live MCP tool descriptions") + parser.add_argument("--module", help="Only generate for this module (default: all in-scope)") + parser.add_argument("--dry-run", action="store_true", help="Print generated cases without writing to disk") + parser.add_argument("--overwrite", action="store_true", help="Replace existing happy_path cases (default: skip if already present)") + args = parser.parse_args() + + mcp_url = os.environ.get("MCP_SERVER_URL", "http://127.0.0.1:8001/mcp") + model_id = os.environ.get("BEDROCK_MODEL_ID", "anthropic.claude-3-5-sonnet-20241022-v2:0") + target_modules = {args.module} if args.module else IN_SCOPE_MODULES + + print(f"Connecting to MCP server at {mcp_url} ...") + tools = asyncio.run(collect_tools(mcp_url)) + print(f"Found {len(tools)} tools total") + + bedrock = make_bedrock_client() + + # Group tools by module + by_module: dict[str, list] = {m: [] for m in target_modules} + for tool in tools: + mod = get_module(tool.name) + if mod in target_modules: + by_module[mod].append(tool) + + for module, module_tools in by_module.items(): + if not module_tools: + print(f"\n[{module}] no tools found — skipping") + continue + + print(f"\n[{module}] {len(module_tools)} tools") + existing = load_existing(module) + existing_ids = {c["id"] for c in existing} + new_cases = list(existing) + + for tool in module_tools: + case_id = f"{tool.name}_happy" + if case_id in existing_ids and not args.overwrite: + print(f" Skipping {tool.name} (already has happy_path case)") + continue + print(f" Drafting case for {tool.name} ...") + case = draft_case(tool, bedrock, model_id) + if case is None: + continue + # Replace existing happy_path if overwrite, else append + new_cases = [c for c in new_cases if c["id"] != case_id] + new_cases.append(case) + + if args.dry_run: + print(json.dumps({"module": module, "cases": new_cases}, indent=2)) + else: + write_cases(module, new_cases) + + print("\nDone. Review generated cases, then hand-author ambiguous_selection, missing_parameter, and multi_tool cases.") + + +if __name__ == "__main__": + main() diff --git a/tests/eval_test/backup/inspect_tools.py b/tests/eval_test/backup/inspect_tools.py new file mode 100644 index 0000000..10ebb39 --- /dev/null +++ b/tests/eval_test/backup/inspect_tools.py @@ -0,0 +1,92 @@ +""" +Dump live MCP tool descriptions to results/live_descriptions_.json. + +Usage: + uv run python backup/inspect_tools.py # all base tools + uv run python backup/inspect_tools.py --module base # explicit module filter + uv run python backup/inspect_tools.py --all-modules # every module on the server + +Output: results/live_descriptions_.json (one file per module) +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import os +from pathlib import Path + +from dotenv import load_dotenv +from mcp import ClientSession +from mcp.client.streamable_http import streamablehttp_client + +load_dotenv() + +RESULTS_DIR = Path(__file__).resolve().parent.parent / "results" +DEFAULT_MODULES = {"base"} + + +async def fetch_tools(mcp_url: str) -> list[dict]: + async with streamablehttp_client(mcp_url) as (read, write, _): + async with ClientSession(read, write) as session: + await session.initialize() + result = await session.list_tools() + return [ + { + "name": t.name, + "description": t.description or "", + "inputSchema": t.inputSchema, + } + for t in result.tools + ] + + +def group_by_module(tools: list[dict]) -> dict[str, list[dict]]: + grouped: dict[str, list[dict]] = {} + for tool in tools: + module = tool["name"].split("_")[0] + grouped.setdefault(module, []).append(tool) + return grouped + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dump live MCP tool descriptions.") + parser.add_argument("--module", default=None, help="Filter to a single module prefix (e.g. base)") + parser.add_argument("--all-modules", action="store_true", help="Include all modules (default: base only)") + args = parser.parse_args() + + mcp_url = os.environ.get("MCP_SERVER_URL", "http://127.0.0.1:8001/mcp") + print(f"Fetching tools from {mcp_url} ...") + + tools = asyncio.run(fetch_tools(mcp_url)) + grouped = group_by_module(tools) + + if args.module: + target_modules = {args.module} + elif args.all_modules: + target_modules = set(grouped.keys()) + else: + target_modules = DEFAULT_MODULES + + RESULTS_DIR.mkdir(exist_ok=True) + + for module, module_tools in sorted(grouped.items()): + if module not in target_modules: + continue + + out_path = RESULTS_DIR / f"live_descriptions_{module}.json" + payload = { + "module": module, + "mcp_url": mcp_url, + "tool_count": len(module_tools), + "tools": module_tools, + } + out_path.write_text(json.dumps(payload, indent=2)) + print(f" [{module}] {len(module_tools)} tools → {out_path}") + + print("Done.") + + +if __name__ == "__main__": + main() diff --git a/tests/eval_test/cases/base.json b/tests/eval_test/cases/base.json new file mode 100644 index 0000000..e72afb6 --- /dev/null +++ b/tests/eval_test/cases/base.json @@ -0,0 +1,460 @@ +{ + "module": "base", + "cases": [ + { + "id": "base_readQuery_happy", + "type": "happy_path", + "description": "Execute a SQL query against the evals employees table", + "input": "Run this query: SELECT name, department, salary FROM {EVALS_DATABASE}.evals_employees WHERE department = 'Sales'", + "expected_tools": [ + { + "name": "base_readQuery", + "params": { + "sql": "SELECT name, department, salary FROM {EVALS_DATABASE}.evals_employees WHERE department = 'Sales'" + } + } + ] + }, + { + "id": "base_readQuery_ambiguous_vs_tablePreview", + "type": "ambiguous_selection", + "description": "Prompt with a WHERE clause should use base_readQuery, not base_tablePreview", + "input": "Get me all orders from {EVALS_DATABASE}.evals_orders where the amount is greater than 500", + "expected_tools": [ + { + "name": "base_readQuery", + "params": { + "sql": "SELECT * FROM {EVALS_DATABASE}.evals_orders WHERE amount > 500" + } + } + ] + }, + { + "id": "base_readQuery_missing_sql", + "type": "missing_parameter", + "description": "Agent must ask for clarification when no SQL or table is given", + "input": "Query the database for me", + "expected_tools": [] + }, + { + "id": "base_tableList_happy", + "type": "happy_path", + "description": "List tables in the evals database", + "input": "What tables are in the {EVALS_DATABASE} database?", + "expected_tools": [ + { + "name": "base_tableList", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "base_tableList_missing_database", + "type": "missing_parameter", + "description": "User asks for tables without specifying a database", + "input": "Show me what tables are available", + "expected_tools": [] + }, + { + "id": "base_tableDDL_happy", + "type": "happy_path", + "description": "Retrieve DDL for the evals employees table", + "input": "Show me the CREATE statement for evals_employees in the {EVALS_DATABASE} database", + "expected_tools": [ + { + "name": "base_tableDDL", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_employees" + } + } + ] + }, + { + "id": "base_tableDDL_ambiguous_vs_columnMetadata", + "type": "ambiguous_selection", + "description": "Asking for full structure including constraints should prefer base_tableDDL", + "input": "Describe the full structure of {EVALS_DATABASE}.evals_employees including any constraints and indexes", + "expected_tools": [ + { + "name": "base_tableDDL", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_employees" + } + } + ] + }, + { + "id": "base_columnMetadata_happy", + "type": "happy_path", + "description": "Get precise Teradata type codes, character sets, and precision for columns in evals_orders", + "input": "Give me the exact Teradata type codes, character set, and decimal precision for every column in {EVALS_DATABASE}.evals_orders", + "expected_tools": [ + { + "name": "base_columnMetadata", + "params": { + "database_name": "{EVALS_DATABASE}", + "object_name": "evals_orders" + } + } + ] + }, + { + "id": "base_columnMetadata_ambiguous_vs_tableDDL", + "type": "ambiguous_selection", + "description": "Asking for technical Teradata column precision metadata should prefer base_columnMetadata over base_tableDDL", + "input": "I need the UNICODE vs LATIN character set and nullability for every column in {EVALS_DATABASE}.evals_employees", + "expected_tools": [ + { + "name": "base_columnMetadata", + "params": { + "database_name": "{EVALS_DATABASE}", + "object_name": "evals_employees" + } + } + ] + }, + { + "id": "base_tablePreview_happy", + "type": "happy_path", + "description": "Preview a sample of rows from the evals orders table", + "input": "Give me a quick preview of what's in {EVALS_DATABASE}.evals_orders", + "expected_tools": [ + { + "name": "base_tablePreview", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_orders" + } + } + ] + }, + { + "id": "base_tablePreview_ambiguous_vs_readQuery", + "type": "ambiguous_selection", + "description": "A simple 'show me a few rows' without a WHERE clause should use base_tablePreview", + "input": "Show me a few rows from {EVALS_DATABASE}.evals_orders", + "expected_tools": [ + { + "name": "base_tablePreview", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_orders" + } + } + ] + }, + { + "id": "base_databaseList_happy", + "type": "happy_path", + "description": "List all accessible databases", + "input": "What databases do I have access to?", + "expected_tools": [ + { + "name": "base_databaseList", + "params": {} + } + ] + }, + { + "id": "base_tableList_ambiguous_vs_databaseList", + "type": "ambiguous_selection", + "description": "Listing tables inside one database should prefer base_tableList, not base_databaseList", + "input": "What tables exist inside the {EVALS_DATABASE} database?", + "expected_tools": [ + { + "name": "base_tableList", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "base_databaseList_ambiguous_vs_tableList", + "type": "ambiguous_selection", + "description": "Listing accessible databases should prefer base_databaseList, not base_tableList", + "input": "Which databases am I allowed to connect to on this system?", + "expected_tools": [ + { + "name": "base_databaseList", + "params": {} + } + ] + }, + { + "id": "base_saveDDL_ambiguous_vs_tableDDL", + "type": "ambiguous_selection", + "description": "Persisting DDL to a file should prefer base_saveDDL, not base_tableDDL", + "input": "Export the DDL for {EVALS_DATABASE}.evals_orders to a file on disk", + "expected_tools": [ + { + "name": "base_saveDDL", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_orders" + } + } + ] + }, + { + "id": "base_tableAffinity_ambiguous_vs_tableUsage", + "type": "ambiguous_selection", + "description": "Finding tables co-queried with a specific table should prefer base_tableAffinity, not base_tableUsage", + "input": "Which tables in {EVALS_DATABASE} tend to appear in the same queries as evals_employees?", + "expected_tools": [ + { + "name": "base_tableAffinity", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_employees" + } + } + ] + }, + { + "id": "base_tableUsage_ambiguous_vs_tableAffinity", + "type": "ambiguous_selection", + "description": "Access frequency for tables in a database should prefer base_tableUsage, not base_tableAffinity", + "input": "Who has been reading tables in {EVALS_DATABASE} lately and how often?", + "expected_tools": [ + { + "name": "base_tableUsage", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "base_saveDDL_happy", + "type": "happy_path", + "description": "Save DDL for the evals employees table to a file", + "input": "Save the DDL for evals_employees in {EVALS_DATABASE} to a file", + "expected_tools": [ + { + "name": "base_saveDDL", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_employees" + } + } + ] + }, + { + "id": "base_tableAffinity_happy", + "type": "happy_path", + "description": "Find tables commonly queried together with evals_orders", + "input": "Which tables in {EVALS_DATABASE} are most often queried together with evals_orders?", + "expected_tools": [ + { + "name": "base_tableAffinity", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_orders" + } + } + ] + }, + { + "id": "base_tableUsage_happy", + "type": "happy_path", + "description": "Check usage metrics for the evals database", + "input": "How frequently are the tables in {EVALS_DATABASE} being accessed and by whom?", + "expected_tools": [ + { + "name": "base_tableUsage", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "base_multi_tool_list_then_preview", + "type": "multi_tool", + "description": "List tables in evals database then preview one", + "input": "List all tables in {EVALS_DATABASE}, then show me a preview of evals_employees", + "expected_tools": [ + { + "name": "base_tableList", + "params": { + "database_name": "{EVALS_DATABASE}" + } + }, + { + "name": "base_tablePreview", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_employees" + } + } + ] + }, + { + "id": "base_multi_tool_ddl_then_columns", + "type": "multi_tool", + "description": "Get DDL then drill into column detail for the same table", + "input": "First show me the DDL for {EVALS_DATABASE}.evals_orders, then give me the detailed column metadata", + "expected_tools": [ + { + "name": "base_tableDDL", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_orders" + } + }, + { + "name": "base_columnMetadata", + "params": { + "database_name": "{EVALS_DATABASE}", + "object_name": "evals_orders" + } + } + ] + }, + { + "id": "base_tableDDL_missing_table", + "type": "missing_parameter", + "description": "Agent must ask for database name when only table is provided", + "input": "Show me the CREATE statement for evals_employees", + "expected_tools": [] + }, + { + "id": "base_tablePreview_missing_table", + "type": "missing_parameter", + "description": "Agent must ask which table to preview", + "input": "Preview some rows", + "expected_tools": [] + }, + { + "id": "base_saveDDL_missing_table", + "type": "missing_parameter", + "description": "Agent must ask which table to save DDL for", + "input": "Save the DDL to a file", + "expected_tools": [] + }, + { + "id": "base_tablePreview_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks which table to preview, then calls base_tablePreview after user clarifies", + "turns": [ + { + "input": "Preview some rows for me", + "expect": "clarification" + }, + { + "input": "Preview rows from {EVALS_DATABASE}.evals_employees", + "expected_tools": [ + { + "name": "base_tablePreview", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_employees" + } + } + ] + } + ] + }, + { + "id": "base_tableList_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks which database, then calls base_tableList after user clarifies", + "turns": [ + { + "input": "Show me what tables are available", + "expect": "clarification" + }, + { + "input": "List tables in the {EVALS_DATABASE} database", + "expected_tools": [ + { + "name": "base_tableList", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + } + ] + }, + { + "id": "base_readQuery_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks for SQL, then calls base_readQuery after user provides the query", + "turns": [ + { + "input": "Run a query for me", + "expect": "clarification" + }, + { + "input": "Run: SELECT name, department FROM {EVALS_DATABASE}.evals_employees WHERE department = 'Sales'", + "expected_tools": [ + { + "name": "base_readQuery", + "params": { + "sql": "SELECT name, department FROM {EVALS_DATABASE}.evals_employees WHERE department = 'Sales'" + } + } + ] + } + ] + }, + { + "id": "base_columnDescription_happy", + "type": "happy_path", + "description": "Show basic column info for the evals employees table", + "input": "Show me the columns in {EVALS_DATABASE}.evals_employees", + "expected_tools": [ + { + "name": "base_columnDescription", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_employees" + } + } + ] + }, + { + "id": "base_columnDescription_ambiguous_vs_columnMetadata", + "type": "ambiguous_selection", + "description": "A simple 'what columns does this table have' question should prefer base_columnDescription", + "input": "What are the column names and types in {EVALS_DATABASE}.evals_orders?", + "expected_tools": [ + { + "name": "base_columnDescription", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_orders" + } + } + ] + }, + { + "id": "base_columnMetadata_ambiguous_vs_columnDescription", + "type": "ambiguous_selection", + "description": "Asking for precise Teradata type codes and character sets should prefer base_columnMetadata", + "input": "Give me the exact Teradata type codes, character sets, and precision details for every column in {EVALS_DATABASE}.evals_employees", + "expected_tools": [ + { + "name": "base_columnMetadata", + "params": { + "database_name": "{EVALS_DATABASE}", + "object_name": "evals_employees" + } + } + ] + }, + { + "id": "base_unknown_tool_missing", + "type": "missing_parameter", + "description": "User asks to do something no tool supports — agent should explain and ask for clarification", + "input": "Automatically tune my Teradata database indexes and rebalance the AMPs", + "expected_tools": [] + } + ] +} diff --git a/tests/eval_test/cases/chat.json b/tests/eval_test/cases/chat.json new file mode 100644 index 0000000..005c8a2 --- /dev/null +++ b/tests/eval_test/cases/chat.json @@ -0,0 +1,19 @@ +{ + "module": "chat", + "cases": [ + { + "id": "chat_completeChat_missing_message", + "type": "missing_parameter", + "description": "User invokes chat without providing a message", + "input": "Can you use the chat completion tool for me?", + "expected_tools": [] + }, + { + "id": "chat_unknown_tool_missing", + "type": "missing_parameter", + "description": "User asks for a non‑existent chat tool", + "input": "Talk to the assistant about budgeting", + "expected_tools": [] + } + ] +} diff --git a/tests/eval_test/cases/dba.json b/tests/eval_test/cases/dba.json new file mode 100644 index 0000000..b6ba1ce --- /dev/null +++ b/tests/eval_test/cases/dba.json @@ -0,0 +1,441 @@ +{ + "module": "dba", + "cases": [ + { + "id": "dba_tableSpace_happy", + "type": "happy_path", + "description": "List largest tables by storage in the evals database", + "input": "Show me the 10 largest tables in {EVALS_DATABASE} ranked by storage consumption.", + "expected_tools": [ + { + "name": "dba_tableSpace", + "params": { + "database_name": "{EVALS_DATABASE}", + "top_n": 10 + } + } + ] + }, + { + "id": "dba_tableSqlList_happy", + "type": "happy_path", + "description": "List SQL executed against evals_orders over the past 14 days", + "input": "Show me all queries run against evals_orders over the last 14 days.", + "expected_tools": [ + { + "name": "dba_tableSqlList", + "params": { + "table_name": "evals_orders", + "no_days": 14 + } + } + ] + }, + { + "id": "dba_userSqlList_happy", + "type": "happy_path", + "description": "Show queries executed by a specific user over the past 14 days", + "input": "Show me what queries John_Smith has been executing over the past 14 days.", + "expected_tools": [ + { + "name": "dba_userSqlList", + "params": { + "user_name": "John_Smith", + "no_days": 14 + } + } + ] + }, + { + "id": "dba_databaseSpace_happy", + "type": "happy_path", + "description": "Storage allocated to the evals database", + "input": "How much disk space is allocated to the {EVALS_DATABASE} database?", + "expected_tools": [ + { + "name": "dba_databaseSpace", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "dba_tableUsageImpact_happy", + "type": "happy_path", + "description": "Which users are consuming the most resources in the evals database", + "input": "Which users are consuming the most resources in {EVALS_DATABASE}?", + "expected_tools": [ + { + "name": "dba_tableUsageImpact", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "dba_resusageSummary_happy", + "type": "happy_path", + "description": "System-wide CPU/memory/IO by application over the past month", + "input": "Show me how much CPU, memory, and I/O was consumed over the past month, grouped by application.", + "expected_tools": [ + { + "name": "dba_resusageSummary", + "params": { + "no_days": 30 + } + } + ] + }, + { + "id": "dba_databaseVersion_happy", + "type": "happy_path", + "description": "Get current Teradata software version", + "input": "What release of Teradata software is currently running on our system?", + "expected_tools": [ + { + "name": "dba_databaseVersion", + "params": {} + } + ] + }, + { + "id": "dba_flowControl_happy", + "type": "happy_path", + "description": "System throttling and delay statistics for a date range", + "input": "Show me the system throttling and flow control delay statistics from January 15th 2024 through February 28th 2024.", + "expected_tools": [ + { + "name": "dba_flowControl", + "params": { + "start_date": "2024-01-15", + "end_date": "2024-02-28" + } + } + ] + }, + { + "id": "dba_featureUsage_happy", + "type": "happy_path", + "description": "Feature utilization metrics for a date range", + "input": "Show me how different Teradata features were being utilized by our users between January 15th, 2024 and March 31st, 2024.", + "expected_tools": [ + { + "name": "dba_featureUsage", + "params": { + "start_date": "2024-01-15", + "end_date": "2024-03-31" + } + } + ] + }, + { + "id": "dba_userDelay_happy", + "type": "happy_path", + "description": "How long users waited in queues for a date range", + "input": "Show me how long users were waiting in the query queue between March 1st 2024 and March 31st 2024.", + "expected_tools": [ + { + "name": "dba_userDelay", + "params": { + "start_date": "2024-03-01", + "end_date": "2024-03-31" + } + } + ] + }, + { + "id": "dba_sessionInfo_happy", + "type": "happy_path", + "description": "Active sessions for a specific user", + "input": "Show me what active connections the database user SALES_ADMIN currently has open in Teradata.", + "expected_tools": [ + { + "name": "dba_sessionInfo", + "params": { + "user_name": "SALES_ADMIN" + } + } + ] + }, + { + "id": "dba_systemSpace_happy", + "type": "happy_path", + "description": "Total disk consumption across all databases", + "input": "How much disk capacity is currently being consumed across all databases in our data warehouse?", + "expected_tools": [ + { + "name": "dba_systemSpace", + "params": {} + } + ] + }, + { + "id": "dba_tableSpace_ambiguous_vs_databaseSpace", + "type": "ambiguous_selection", + "description": "Ranking tables by size inside one database should prefer dba_tableSpace, not dba_databaseSpace", + "input": "Which tables in {EVALS_DATABASE} are using the most disk space?", + "expected_tools": [ + { + "name": "dba_tableSpace", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "dba_databaseSpace_ambiguous_vs_systemSpace", + "type": "ambiguous_selection", + "description": "Storage for one named database should prefer dba_databaseSpace, not dba_systemSpace", + "input": "How much total space is the {EVALS_DATABASE} database using?", + "expected_tools": [ + { + "name": "dba_databaseSpace", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "dba_systemSpace_ambiguous_vs_databaseSpace", + "type": "ambiguous_selection", + "description": "Warehouse-wide storage across all databases should prefer dba_systemSpace, not dba_databaseSpace", + "input": "What is the total disk usage across the entire Teradata system?", + "expected_tools": [ + { + "name": "dba_systemSpace", + "params": {} + } + ] + }, + { + "id": "dba_tableSqlList_ambiguous_vs_userSqlList", + "type": "ambiguous_selection", + "description": "Queries run against a table should prefer dba_tableSqlList, not dba_userSqlList", + "input": "What SQL has been executed against evals_orders in the past week?", + "expected_tools": [ + { + "name": "dba_tableSqlList", + "params": { + "table_name": "evals_orders", + "no_days": 7 + } + } + ] + }, + { + "id": "dba_userSqlList_ambiguous_vs_tableSqlList", + "type": "ambiguous_selection", + "description": "Queries run by a specific user should prefer dba_userSqlList, not dba_tableSqlList", + "input": "List all SQL statements that user ANALYTICS_USER has run over the past 7 days.", + "expected_tools": [ + { + "name": "dba_userSqlList", + "params": { + "user_name": "ANALYTICS_USER", + "no_days": 7 + } + } + ] + }, + { + "id": "dba_tableUsageImpact_ambiguous_vs_resusageSummary", + "type": "ambiguous_selection", + "description": "Heavy users of a specific database should prefer dba_tableUsageImpact, not dba_resusageSummary", + "input": "Who is generating the heaviest workload against {EVALS_DATABASE}?", + "expected_tools": [ + { + "name": "dba_tableUsageImpact", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "dba_resusageSummary_ambiguous_vs_tableUsageImpact", + "type": "ambiguous_selection", + "description": "System-wide CPU/memory/I/O by application should prefer dba_resusageSummary, not dba_tableUsageImpact", + "input": "Break down CPU, memory, and I/O consumption by application over the last 30 days.", + "expected_tools": [ + { + "name": "dba_resusageSummary", + "params": { + "no_days": 30 + } + } + ] + }, + { + "id": "dba_tableUsageImpact_ambiguous_vs_sessionInfo", + "type": "ambiguous_selection", + "description": "Historical resource consumption by users in a database should prefer dba_tableUsageImpact, not dba_sessionInfo", + "input": "Which users have been consuming the most CPU and query resources in {EVALS_DATABASE} over time?", + "expected_tools": [ + { + "name": "dba_tableUsageImpact", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + }, + { + "id": "dba_sessionInfo_ambiguous_vs_tableUsageImpact", + "type": "ambiguous_selection", + "description": "Open sessions for a specific user should prefer dba_sessionInfo, not dba_tableUsageImpact", + "input": "What active sessions does user SALES_ADMIN currently have open?", + "expected_tools": [ + { + "name": "dba_sessionInfo", + "params": { + "user_name": "SALES_ADMIN" + } + } + ] + }, + { + "id": "dba_userDelay_ambiguous_vs_flowControl", + "type": "ambiguous_selection", + "description": "How long users waited in queues should prefer dba_userDelay, not dba_flowControl", + "input": "How long were users waiting in the query queue between March 1 and March 31, 2024?", + "expected_tools": [ + { + "name": "dba_userDelay", + "params": { + "start_date": "2024-03-01", + "end_date": "2024-03-31" + } + } + ] + }, + { + "id": "dba_flowControl_ambiguous_vs_userDelay", + "type": "ambiguous_selection", + "description": "System throttling and flow-control statistics should prefer dba_flowControl, not dba_userDelay", + "input": "Show throttling and flow-control delay statistics from January 15 through February 28, 2024.", + "expected_tools": [ + { + "name": "dba_flowControl", + "params": { + "start_date": "2024-01-15", + "end_date": "2024-02-28" + } + } + ] + }, + { + "id": "dba_tableSpace_missing_db", + "type": "missing_parameter", + "description": "Agent must ask which database to list biggest tables for", + "input": "Show me the biggest tables", + "expected_tools": [] + }, + { + "id": "dba_tableSqlList_missing_table", + "type": "missing_parameter", + "description": "Agent must ask which table to list queries for", + "input": "Show me the queries from the last 14 days", + "expected_tools": [] + }, + { + "id": "dba_userSqlList_missing_user", + "type": "missing_parameter", + "description": "Agent must ask which user to list queries for", + "input": "Show me the queries a user executed over the past 14 days", + "expected_tools": [] + }, + { + "id": "dba_databaseSpace_missing_db", + "type": "missing_parameter", + "description": "Agent must ask which database to get storage for", + "input": "How much space is a specific database using?", + "expected_tools": [] + }, + { + "id": "dba_tableUsageImpact_missing_db", + "type": "missing_parameter", + "description": "Agent must ask which database to evaluate usage impact for", + "input": "Which users are hitting a particular database the most?", + "expected_tools": [] + }, + { + "id": "dba_databaseSpace_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks which database, then calls dba_databaseSpace after user clarifies", + "turns": [ + { + "input": "How much space is a specific database using?", + "expect": "clarification" + }, + { + "input": "How much disk space is the {EVALS_DATABASE} database using?", + "expected_tools": [ + { + "name": "dba_databaseSpace", + "params": { + "database_name": "{EVALS_DATABASE}" + } + } + ] + } + ] + }, + { + "id": "dba_tableSqlList_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks which table, then calls dba_tableSqlList after user clarifies", + "turns": [ + { + "input": "Show me the queries from the last 14 days", + "expect": "clarification" + }, + { + "input": "List SQL run against evals_orders over the past 14 days", + "expected_tools": [ + { + "name": "dba_tableSqlList", + "params": { + "table_name": "evals_orders", + "no_days": 14 + } + } + ] + } + ] + }, + { + "id": "dba_userSqlList_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks which user, then calls dba_userSqlList after user clarifies", + "turns": [ + { + "input": "Show me the queries a user executed over the past 14 days", + "expect": "clarification" + }, + { + "input": "List all SQL that user ANALYTICS_USER has run over the last 14 days", + "expected_tools": [ + { + "name": "dba_userSqlList", + "params": { + "user_name": "ANALYTICS_USER", + "no_days": 14 + } + } + ] + } + ] + }, + { + "id": "dba_unknown_tool_missing", + "type": "missing_parameter", + "description": "User asks for something no DBA tool supports — agent should explain and ask for clarification", + "input": "Automatically redistribute my table data across AMPs to improve skew", + "expected_tools": [] + } + ] +} diff --git a/tests/eval_test/cases/plot.json b/tests/eval_test/cases/plot.json new file mode 100644 index 0000000..1d8df39 --- /dev/null +++ b/tests/eval_test/cases/plot.json @@ -0,0 +1,138 @@ +{ + "module": "plot", + "cases": [ + { + "id": "plot_line_chart_happy", + "type": "happy_path", + "description": "Line chart of order amounts over time from evals_orders", + "input": "Show me a line chart of order amounts over time from {EVALS_DATABASE}.evals_orders using order_date on the x-axis and amount on the y-axis", + "expected_tools": [ + { + "name": "plot_line_chart", + "params": { + "table_name": "{EVALS_DATABASE}.evals_orders", + "labels": "order_date", + "columns": ["amount"] + } + } + ] + }, + { + "id": "plot_pie_chart_happy", + "type": "happy_path", + "description": "Pie chart breaking down orders by product category", + "input": "Plot {EVALS_DATABASE}.evals_orders as a pie chart using product_category for the slice labels and amount for the values.", + "expected_tools": [ + { + "name": "plot_pie_chart", + "params": { + "table_name": "{EVALS_DATABASE}.evals_orders", + "labels": "product_category", + "column": "amount" + } + } + ] + }, + { + "id": "plot_polar_chart_happy", + "type": "happy_path", + "description": "Polar area chart of order amounts by product category", + "input": "Create a polar area chart showing order amounts by product_category from {EVALS_DATABASE}.evals_orders", + "expected_tools": [ + { + "name": "plot_polar_chart", + "params": { + "table_name": "{EVALS_DATABASE}.evals_orders", + "labels": "product_category", + "column": "amount" + } + } + ] + }, + { + "id": "plot_radar_chart_happy", + "type": "happy_path", + "description": "Radar chart of salary by department in evals_employees", + "input": "Draw a spider chart from {EVALS_DATABASE}.evals_employees using department as the axis labels and salary as the values.", + "expected_tools": [ + { + "name": "plot_radar_chart", + "params": { + "table_name": "{EVALS_DATABASE}.evals_employees", + "labels": "department", + "columns": "salary" + } + } + ] + }, + { + "id": "plot_line_vs_radar_ambiguous", + "type": "ambiguous_selection", + "description": "Time-series data should use line chart, not radar", + "input": "Plot how order amounts change over time from {EVALS_DATABASE}.evals_orders using order_date on the x-axis", + "expected_tools": [ + { + "name": "plot_line_chart", + "params": { + "table_name": "{EVALS_DATABASE}.evals_orders", + "labels": "order_date", + "columns": ["amount"] + } + } + ] + }, + { + "id": "plot_pie_vs_polar_ambiguous", + "type": "ambiguous_selection", + "description": "Proportion/share breakdown should use pie chart over polar", + "input": "Use a pie chart to show {EVALS_DATABASE}.evals_orders with product_category as labels and amount as the values.", + "expected_tools": [ + { + "name": "plot_pie_chart", + "params": { + "table_name": "{EVALS_DATABASE}.evals_orders", + "labels": "product_category", + "column": "amount" + } + } + ] + }, + { + "id": "plot_missing_data", + "type": "missing_parameter", + "description": "Chart request with no table or data provided \u2014 agent must ask for clarification", + "input": "Create a chart for me", + "expected_tools": [] + }, + { + "id": "plot_pie_chart_direct", + "type": "happy_path", + "description": "Direct pie chart from table — no pre-query needed since plot tools read tables directly", + "input": "Give me a pie chart of amount by product_category from {EVALS_DATABASE}.evals_orders", + "expected_tools": [ + { + "name": "plot_pie_chart", + "params": { + "table_name": "{EVALS_DATABASE}.evals_orders", + "labels": "product_category", + "column": "amount" + } + } + ] + }, + { + "id": "plot_missing_table", + "type": "missing_parameter", + "description": "Agent must ask which table to plot", + "input": "Create a line chart of sales over time", + "expected_tools": [] + }, + { + "id": "plot_unknown_tool_missing", + "type": "missing_parameter", + "description": "User asks for a non\u2011existent plot tool", + "input": "Show me the network topology as a diagram", + "expected_tools": [] + } + ] +} diff --git a/tests/eval_test/cases/qlty.json b/tests/eval_test/cases/qlty.json new file mode 100644 index 0000000..b08c6ab --- /dev/null +++ b/tests/eval_test/cases/qlty.json @@ -0,0 +1,183 @@ +{ + "module": "qlty", + "cases": [ + { + "id": "qlty_missingValues_happy", + "type": "happy_path", + "description": "Find columns with nulls in evals_orders (ship_date is nullable)", + "input": "Which columns in {EVALS_DATABASE}.evals_orders contain null or empty entries?", + "expected_tools": [ + { + "name": "qlty_missingValues", + "params": {} + } + ] + }, + { + "id": "qlty_missingValues_ambiguous_vs_rowsWithMissingValues", + "type": "ambiguous_selection", + "description": "Asking which columns have nulls (column-level summary) should prefer qlty_missingValues over qlty_rowsWithMissingValues", + "input": "Which fields in {EVALS_DATABASE}.evals_orders have missing data?", + "expected_tools": [ + { + "name": "qlty_missingValues", + "params": {} + } + ] + }, + { + "id": "qlty_negativeValues_happy", + "type": "happy_path", + "description": "Find negative amounts in evals_orders (some amounts are negative refunds)", + "input": "Show me which fields in {EVALS_DATABASE}.evals_orders contain numbers below zero", + "expected_tools": [ + { + "name": "qlty_negativeValues", + "params": {} + } + ] + }, + { + "id": "qlty_distinctCategories_happy", + "type": "happy_path", + "description": "List unique product categories in evals_orders", + "input": "Show me all the unique values in the product_category column of {EVALS_DATABASE}.evals_orders", + "expected_tools": [ + { + "name": "qlty_distinctCategories", + "params": { + "column_name": "product_category" + } + } + ] + }, + { + "id": "qlty_distinctCategories_missing_column", + "type": "missing_parameter", + "description": "Agent must ask which column when none is specified", + "input": "What distinct values exist in a column of {EVALS_DATABASE}.evals_orders?", + "expected_tools": [] + }, + { + "id": "qlty_standardDeviation_happy", + "type": "happy_path", + "description": "Get mean and spread of salary in evals_employees", + "input": "What is the average and spread of salary values in {EVALS_DATABASE}.evals_employees?", + "expected_tools": [ + { + "name": "qlty_standardDeviation", + "params": { + "column_name": "salary" + } + } + ] + }, + { + "id": "qlty_standardDeviation_ambiguous_vs_univariateStatistics", + "type": "ambiguous_selection", + "description": "Asking specifically for standard deviation should prefer qlty_standardDeviation over the broader qlty_univariateStatistics", + "input": "What is the standard deviation of the salary field in {EVALS_DATABASE}.evals_employees?", + "expected_tools": [ + { + "name": "qlty_standardDeviation", + "params": { + "column_name": "salary" + } + } + ] + }, + { + "id": "qlty_columnSummary_happy", + "type": "happy_path", + "description": "Get a statistical summary of all columns in evals_employees", + "input": "Show me a statistical summary of every field in {EVALS_DATABASE}.evals_employees", + "expected_tools": [ + { + "name": "qlty_columnSummary", + "params": {} + } + ] + }, + { + "id": "qlty_univariateStatistics_happy", + "type": "happy_path", + "description": "Get full univariate stats for salary in evals_employees", + "input": "Give me a complete statistical breakdown of the salary field in {EVALS_DATABASE}.evals_employees", + "expected_tools": [ + { + "name": "qlty_univariateStatistics", + "params": { + "column_name": "salary" + } + } + ] + }, + { + "id": "qlty_univariateStatistics_ambiguous_vs_columnSummary", + "type": "ambiguous_selection", + "description": "Asking for stats on a specific column should prefer qlty_univariateStatistics over the table-level qlty_columnSummary", + "input": "Show me min, max, mean, and percentiles for the amount column in {EVALS_DATABASE}.evals_orders", + "expected_tools": [ + { + "name": "qlty_univariateStatistics", + "params": { + "column_name": "amount" + } + } + ] + }, + { + "id": "qlty_rowsWithMissingValues_happy", + "type": "happy_path", + "description": "Retrieve rows where ship_date is null in evals_orders", + "input": "Show me all records from {EVALS_DATABASE}.evals_orders where the ship_date is empty or null", + "expected_tools": [ + { + "name": "qlty_rowsWithMissingValues", + "params": { + "column_name": "ship_date" + } + } + ] + }, + { + "id": "qlty_rowsWithMissingValues_ambiguous_vs_missingValues", + "type": "ambiguous_selection", + "description": "Asking to retrieve the actual rows with missing data should prefer qlty_rowsWithMissingValues over qlty_missingValues", + "input": "Fetch the rows from {EVALS_DATABASE}.evals_orders that are missing a ship_date", + "expected_tools": [ + { + "name": "qlty_rowsWithMissingValues", + "params": { + "column_name": "ship_date" + } + } + ] + }, + { + "id": "qlty_multi_tool_summary_then_nulls", + "type": "multi_tool", + "description": "Get column summary then drill into rows with missing values", + "input": "First give me a column summary of {EVALS_DATABASE}.evals_orders, then show me the rows where ship_date is missing", + "expected_tools": [ + { + "name": "qlty_columnSummary", + "params": {} + }, + { + "name": "qlty_rowsWithMissingValues", + "params": { + "column_name": "ship_date" + } + } + ] + }, + { + "id": "qlty_unknown_tool_missing", + "type": "missing_parameter", + "description": "User asks for a non\u2011existent quality tool", + "input": "Give me a data quality score for the dataset", + "expected_tools": [] + } + ] +} diff --git a/tests/eval_test/cases/sec.json b/tests/eval_test/cases/sec.json new file mode 100644 index 0000000..d669b55 --- /dev/null +++ b/tests/eval_test/cases/sec.json @@ -0,0 +1,231 @@ +{ + "module": "sec", + "cases": [ + { + "id": "sec_userDbPermissions_ambiguous_vs_userRoles", + "type": "ambiguous_selection", + "description": "Asking about a user's access rights on databases should prefer sec_userDbPermissions, not sec_userRoles", + "input": "Which databases can the user 'bob' access and what can they do in each one?", + "expected_tools": [ + { + "name": "sec_userDbPermissions", + "params": { + "user_name": "bob" + } + } + ] + }, + { + "id": "sec_userDbPermissions_missing_user", + "type": "missing_parameter", + "description": "User asks about permissions without naming a user", + "input": "Show me the database permissions", + "expected_tools": [] + }, + { + "id": "sec_rolePermissions_ambiguous_vs_userDbPermissions", + "type": "ambiguous_selection", + "description": "Asking about a role's access should use sec_rolePermissions, not sec_userDbPermissions", + "input": "What access rights does the 'analyst' role have across all databases?", + "expected_tools": [ + { + "name": "sec_rolePermissions", + "params": { + "role_name": "analyst" + } + } + ] + }, + { + "id": "sec_userRoles_ambiguous_vs_rolePermissions", + "type": "ambiguous_selection", + "description": "Listing roles assigned to a user should prefer sec_userRoles, not sec_rolePermissions", + "input": "Which roles are currently assigned to user 'diana'?", + "expected_tools": [ + { + "name": "sec_userRoles", + "params": { + "user_name": "diana" + } + } + ] + }, + { + "id": "sec_rolePermissions_ambiguous_vs_userRoles", + "type": "ambiguous_selection", + "description": "Permissions granted to a named role should prefer sec_rolePermissions, not sec_userRoles", + "input": "What database access has the 'analyst' role been granted?", + "expected_tools": [ + { + "name": "sec_rolePermissions", + "params": { + "role_name": "analyst" + } + } + ] + }, + { + "id": "sec_userRoles_ambiguous_vs_userDbPermissions", + "type": "ambiguous_selection", + "description": "Asking about a user's role assignments should prefer sec_userRoles even though permissions is related", + "input": "Tell me which roles have been assigned to user 'diana'", + "expected_tools": [ + { + "name": "sec_userRoles", + "params": { + "user_name": "diana" + } + } + ] + }, + { + "id": "sec_multi_tool_user_roles_then_role_permissions", + "type": "multi_tool", + "description": "Find a user's roles, then inspect a specific named role's permissions in one pass", + "input": "Show me what roles 'alice' has, then list the permissions for the 'analyst' role", + "expected_tools": [ + { + "name": "sec_userRoles", + "params": { + "user_name": "alice" + } + }, + { + "name": "sec_rolePermissions", + "params": { + "role_name": "analyst" + } + } + ] + }, + { + "id": "sec_userDbPermissions_happy", + "type": "happy_path", + "description": "Show what databases a user can access and what they can do in each", + "input": "Show me what access rights jsmith has across all the different databases in the system.", + "expected_tools": [ + { + "name": "sec_userDbPermissions", + "params": { + "user_name": "jsmith" + } + } + ] + }, + { + "id": "sec_rolePermissions_happy", + "type": "happy_path", + "description": "Show what permissions a named role has", + "input": "What access rights are granted to the FinanceReporter role?", + "expected_tools": [ + { + "name": "sec_rolePermissions", + "params": { + "role_name": "FinanceReporter" + } + } + ] + }, + { + "id": "sec_userRoles_happy", + "type": "happy_path", + "description": "List the roles assigned to a user account", + "input": "Which roles are assigned to the database account jsmith?", + "expected_tools": [ + { + "name": "sec_userRoles", + "params": { + "user_name": "jsmith" + } + } + ] + }, + { + "id": "sec_rolePermissions_missing_role", + "type": "missing_parameter", + "description": "Agent must ask which role to inspect", + "input": "What access rights does the role have?", + "expected_tools": [] + }, + { + "id": "sec_userRoles_missing_user", + "type": "missing_parameter", + "description": "Agent must ask which user to retrieve roles for", + "input": "Show me the user roles", + "expected_tools": [] + }, + { + "id": "sec_userRoles_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks which user, then calls sec_userRoles after user clarifies", + "turns": [ + { + "input": "Show me the user roles", + "expect": "clarification" + }, + { + "input": "Which roles are assigned to user diana?", + "expected_tools": [ + { + "name": "sec_userRoles", + "params": { + "user_name": "diana" + } + } + ] + } + ] + }, + { + "id": "sec_rolePermissions_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks which role, then calls sec_rolePermissions after user clarifies", + "turns": [ + { + "input": "What access rights does the role have?", + "expect": "clarification" + }, + { + "input": "What database access has the analyst role been granted?", + "expected_tools": [ + { + "name": "sec_rolePermissions", + "params": { + "role_name": "analyst" + } + } + ] + } + ] + }, + { + "id": "sec_userDbPermissions_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks which user, then calls sec_userDbPermissions after user clarifies", + "turns": [ + { + "input": "Show me the database permissions", + "expect": "clarification" + }, + { + "input": "What databases can user bob access and what can they do in each?", + "expected_tools": [ + { + "name": "sec_userDbPermissions", + "params": { + "user_name": "bob" + } + } + ] + } + ] + }, + { + "id": "sec_unknown_tool_missing", + "type": "missing_parameter", + "description": "Vague access question with no user or role named — agent should ask for clarification", + "input": "What access does this user have in the system?", + "expected_tools": [] + } + ] +} diff --git a/tests/eval_test/cases/tmpl.json b/tests/eval_test/cases/tmpl.json new file mode 100644 index 0000000..a02ace3 --- /dev/null +++ b/tests/eval_test/cases/tmpl.json @@ -0,0 +1,9 @@ +{ + "module": "tmpl", + "cases": [ + { + "_comment": "Happy path cases are auto-generated by generate_cases.py — run it first, then add ambiguous_selection, missing_parameter, and multi_tool cases here by hand.", + "_instructions": "After running: uv run python generate_cases.py --module tmpl, edit this file to add edge case types." + } + ] +} diff --git a/tests/eval_test/description_overrides.json b/tests/eval_test/description_overrides.json new file mode 100644 index 0000000..44047bb --- /dev/null +++ b/tests/eval_test/description_overrides.json @@ -0,0 +1,4 @@ +{ + "_comment": "Dev-space description overrides. Gitignored. Edit freely, then promote to MCP server.", + "base_tableList": "Lists all tables and views within a specific Teradata database or schema. The database_name parameter is REQUIRED and must contain an explicit database name provided by the user \u2014 you must NOT call this tool with an empty string or without a database name. If the user asks to see tables but does not specify which database, ask them to clarify which database they want to list tables from before calling this tool. Use base_databaseList instead if the user is asking which databases exist on the system." +} diff --git a/tests/eval_test/docs/cases.md b/tests/eval_test/docs/cases.md new file mode 100644 index 0000000..fb55e92 --- /dev/null +++ b/tests/eval_test/docs/cases.md @@ -0,0 +1,97 @@ +# Cases & scoring + +## What case types test + +| Type | What it catches | +|---|---| +| `happy_path` | Agent picks the right tool with correct params | +| `ambiguous_selection` | Two tools could apply — descriptions must be distinct | +| `missing_parameter` | Agent must ask for clarification, not hallucinate | +| `multi_tool` | Chained tool calls in order | +| `multi_turn` | Shallow dialog — clarify first, then call the right tool (2–7 turns) | + +**Priority modules:** `base`, `dba`, `sec`, `qlty` · **Maintained:** `chat`, `plot`, `tmpl` + +Cases live in `cases/.json`. + +## Single-turn format + +```json +{ + "id": "base_readQuery_happy", + "type": "happy_path", + "description": "What this tests", + "input": "The natural language prompt sent to the agent", + "expected_tools": [ + { + "name": "base_readQuery", + "params": { "sql": "SELECT * FROM {EVALS_DATABASE}.evals_employees SAMPLE 10" } + } + ] +} +``` + +- `missing_parameter`: set `expected_tools` to `[]` +- `multi_tool`: list expected tool calls in order +- Param names must match live MCP schemas (e.g. `sql` for `base_readQuery`) + +## Multi-turn format (optional) + +Use a `turns` array instead of top-level `input` / `expected_tools`. Scored in one MCP session. **2–7 turns.** + +```json +{ + "id": "base_tablePreview_clarify_then_call", + "type": "missing_parameter", + "description": "Agent asks which table, then previews after user clarifies", + "turns": [ + { "input": "Preview some rows for me", "expect": "clarification" }, + { + "input": "Preview rows from {EVALS_DATABASE}.evals_employees", + "expected_tools": [ + { + "name": "base_tablePreview", + "params": { + "database_name": "{EVALS_DATABASE}", + "table_name": "evals_employees" + } + } + ] + } + ] +} +``` + +Each turn sets **exactly one** of `"expect": "clarification"` or non-empty `"expected_tools"`. IDs should contain `clarify_then_call` for `--type multi_turn` filtering. + +## Scoring + +Deterministic checks run first (`judge/checks.py`), then LLM judge metrics where needed. + +### Deterministic checks + +| Check | Applies to | +|---|---| +| No tool calls | `missing_parameter` and clarification turns | +| Exact tool name | Primary tool on happy/ambiguous; all tools on `multi_tool` | +| Exact param values | `database_name`, `table_name`, `column_name`, `user_name`, `role_name` | +| Param key presence | `sql` / `query` — key required; value judged by LLM | + +Structural failures fail immediately without invoking the judge. + +### LLM judge metrics + +| Metric | Applies to | +|---|---| +| `ToolCorrectnessMetric` | Cases with `expected_tools` | +| `Clarification Check` (GEval) | `missing_parameter` and clarification turns | + +## Adding cases for a new tool + +1. Add a `happy_path` case (or use [`backup/generate_cases.py`](../backup/README.md) to draft from live descriptions). +2. Add `ambiguous_selection` — overlapping tool pair, prompt that could call either, `expected_tools` set to the winner. +3. Add `missing_parameter` — vague prompt, `expected_tools: []`. +4. Optionally add `multi_turn` or `multi_tool` cases. +5. Optionally run [`backup/audit_cases.py`](../backup/README.md) for pair coverage. + +Use vocabulary **different from tool descriptions** — evals stress-test routing, not parrot descriptions. diff --git a/tests/eval_test/docs/setup.md b/tests/eval_test/docs/setup.md new file mode 100644 index 0000000..dc07fbd --- /dev/null +++ b/tests/eval_test/docs/setup.md @@ -0,0 +1,94 @@ +# Setup + +## Prerequisites + +- Python 3.11+ +- Teradata MCP Server running at `http://127.0.0.1:8001` connected to a ClearScape Analytics Experience instance +- AWS account with Bedrock access to an Anthropic Claude model + +## Install + +### Option A — uv (recommended) + +[uv](https://docs.astral.sh/uv/) manages the virtual environment automatically. + +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh + +git clone +cd teradata-mcp-evals + +uv venv +uv sync +cp .env.example .env +``` + +With the venv active you can call scripts directly (`python run_evals.py`). Alternatively, prefix commands with `uv run`. + +### Option B — standard venv + pip + +```bash +git clone +cd teradata-mcp-evals + +python3 -m venv .venv +source .venv/bin/activate +pip install -e . +cp .env.example .env +``` + +## Environment variables + +Edit `.env`: + +```dotenv +AWS_REGION=us-east-1 +AWS_ACCESS_KEY_ID=your-access-key-id +AWS_SECRET_ACCESS_KEY=your-secret-access-key +BEDROCK_MODEL_ID=anthropic.claude-3-5-sonnet-20241022-v2:0 + +# Optional — defaults to BEDROCK_MODEL_ID +# BEDROCK_JUDGE_MODEL_ID=anthropic.claude-3-5-sonnet-20241022-v2:0 + +MCP_SERVER_URL=http://127.0.0.1:8001/mcp +EVALS_DATABASE=your_database_name + +AGENT_MAX_STEPS=5 +AGENT_MAX_STEPS_PER_TURN=3 + +# Optional — only when testing description overrides (see docs/workflow.md) +# USE_DESCRIPTION_OVERRIDES=1 +# DESCRIPTION_OVERRIDES_FILE=description_overrides.json +``` + +Credentials follow the standard boto3 chain — env vars, `~/.aws/credentials`, or an IAM instance profile. Set `AWS_SESSION_TOKEN` for temporary credentials. + +## Test data + +Create eval tables once before running live evals: + +```bash +python setup_test_data.py +``` + +| Table | Purpose | +|---|---| +| `evals_employees` | Used by base, qlty, plot cases | +| `evals_orders` | Used by base, qlty, plot, dba cases (includes nullable `ship_date`, negative `amount`) | + +Set `EVALS_DATABASE` to the database where tables are created — usually your ClearScape username. Case JSON uses `{EVALS_DATABASE}` as a runtime placeholder. + +```bash +python teardown_test_data.py # clean up +python setup_test_data.py --drop-first # recreate from scratch +python preflight.py # verify tables exist (also run by run_evals.py) +``` + +## Unit tests + +No MCP server or Bedrock required: + +```bash +uv run pytest tests/test_checks.py tests/test_multi_turn.py tests/test_report.py \ + tests/test_suggest_overrides.py tests/test_description_overrides.py -v +``` diff --git a/tests/eval_test/docs/structure.md b/tests/eval_test/docs/structure.md new file mode 100644 index 0000000..c49216c --- /dev/null +++ b/tests/eval_test/docs/structure.md @@ -0,0 +1,35 @@ +# Project structure + +``` +teradata-mcp-evals/ + agent/ + client.py # MCP agent; optional description overrides (opt-in) + judge/ + bedrock_llm.py # Bedrock wrapper for deepeval judge + checks.py # Deterministic structural checks + metrics.py # ToolCorrectnessMetric + clarification GEval + report.py # Eval summaries → results/ + suggest_overrides.py # LLM draft overrides (library) + cases/ + *.json # Eval cases per MCP module prefix + tests/ + conftest.py # Fixtures, {EVALS_DATABASE} substitution + case_runner.py # Case execution and scoring + test_.py # One pytest file per module + test_checks.py # Unit tests — deterministic checks + test_multi_turn.py # Unit tests — multi-turn schema + test_report.py # Unit tests — eval summaries + test_suggest_overrides.py + test_description_overrides.py + docs/ # Extended documentation + backup/ # Optional bootstrap/audit scripts + run_evals.py # CLI entry point + suggest_overrides.py # CLI — draft description overrides + setup_test_data.py + preflight.py + teardown_test_data.py + pyproject.toml + .env.example +``` + +Optional scripts in [`backup/`](../backup/README.md) are not required for the main eval workflow. diff --git a/tests/eval_test/docs/workflow.md b/tests/eval_test/docs/workflow.md new file mode 100644 index 0000000..e452763 --- /dev/null +++ b/tests/eval_test/docs/workflow.md @@ -0,0 +1,133 @@ +# Running evals & description workflow + +## End-to-end flow + +The main loop measures **live MCP tool descriptions**, improves wording locally, then promotes changes back to the MCP server. + +``` +setup_test_data.py → run_evals.py (baseline) + ↓ + results/runs//summary.{md,json} + results/latest.json ← pointer to latest run + ↓ + suggest_overrides.py + ↓ + results/runs//suggested_overrides.json + ↓ + suggest_overrides.py --apply + ↓ + description_overrides.json + ↓ + run_evals.py --with-description-overrides + ↓ + promote to MCP server → run_evals.py (baseline again) +``` + +| Step | Command / file | Purpose | +|------|----------------|---------| +| 1. Prepare | `setup_test_data.py`, MCP server running | Eval tables + live tools | +| 2. Baseline | `run_evals.py` | Agent sees **live MCP descriptions**; writes artifacts under `results/runs//` | +| 3. Triage | `results/latest_summary.md` or run directory | Pass/fail counts, eval prompts, recommendations | +| 4. Draft fixes | `suggest_overrides.py` | Bedrock drafts revised descriptions for failed `ambiguous_selection` cases | +| 5. Review | run's `suggested_overrides.json` | Human review — **not applied automatically** | +| 6. Apply | `uv run python suggest_overrides.py --apply` | Replace `description_overrides.json` with draft suggestions only | +| 6. Test locally | `description_overrides.json` + `--with-description-overrides` | Patch descriptions without changing the MCP server | +| 7. Ship | Edit descriptions in MCP server repo | Re-run baseline evals to confirm | + +## Running evals + +Before a live run: test tables created, MCP server at `MCP_SERVER_URL`. `run_evals.py` runs preflight automatically. + +```bash +python run_evals.py # all modules +python run_evals.py --module base # one module +python run_evals.py --module base --type ambiguous_selection +python run_evals.py --verbose +python run_evals.py --run-label after-tablelist-fix # optional label in run directory name +python run_evals.py --list-runs # show recent runs +``` + +### `--type` filter + +| Value | Matches case IDs containing | +|---|---| +| `happy_path` | `happy` | +| `ambiguous_selection` | `ambiguous` | +| `missing_parameter` | `missing` | +| `multi_tool` | `multi_tool` | +| `multi_turn` | `clarify_then_call` | + +Advanced filtering via deepeval/pytest, e.g. `deepeval test run tests/ -k "test_base and tablePreview"`. + +## Baseline vs overrides + +**Default:** evals use live MCP server tool descriptions. + +**Opt-in overrides:** review the draft, then apply with `suggest_overrides.py --apply`, or re-run with: + +```bash +python run_evals.py --module base --with-description-overrides +python run_evals.py --module base --description-overrides-file my_overrides.json +``` + +Or set `USE_DESCRIPTION_OVERRIDES=1` / `DESCRIPTION_OVERRIDES_FILE=...` in `.env`. + +### Suggesting override drafts + +After a baseline run with failures: + +```bash +uv run python suggest_overrides.py +uv run python suggest_overrides.py --type missing_parameter +uv run python suggest_overrides.py --type ambiguous_selection,happy_path +``` + +Processes failed **`ambiguous_selection`**, **`happy_path`**, **`missing_parameter`**, and **`multi_tool`** cases by default. Each type gets a tailored prompt: + +| Case type | What it revises | +|---|---| +| `ambiguous_selection` | Both competing tools — sharpen routing boundaries | +| `happy_path` | Expected tool (and wrong tool if one was called) | +| `missing_parameter` | Tools called prematurely — add “ask first” constraints | +| `multi_tool` | Tools in the workflow — clarify sequencing | + +`missing_parameter` cases where the agent never called a tool (judge-only clarification failure) are **skipped** — those are agent behavior, not description routing. + +Flags: `--summary`, `--output`, `--overrides-file`, `--type`, `--dry-run`. + +Reads the latest run summary (`results/latest.json` → `results/runs//summary.json`), fetches live MCP descriptions, and writes the draft into that run directory (also copied to `results/suggested_overrides.json`). Review the draft, then apply: + +```bash +uv run python suggest_overrides.py --apply +uv run python suggest_overrides.py --apply --dry-run # preview merge +uv run python suggest_overrides.py --apply --tools base_tableList # one tool only +uv run python run_evals.py --with-description-overrides +``` + +## Results layout + +Each eval run gets its own directory under `results/runs/`. The directory name encodes when and how the run was executed, for example: + +`2026-06-23T20-40-34.611558Z__base__overrides` + +Format: `____[__][__]` + +| Path | Contents | +|---|---| +| `results/runs//summary.md` | Human-readable triage report for that run | +| `results/runs//summary.json` | Structured case results | +| `results/runs//manifest.json` | Run metadata and artifact list | +| `results/runs//suggested_overrides.json` | LLM draft from that run's failures (when generated) | +| `results/latest.json` | Pointer to the most recent run | +| `results/index.json` | Newest-first list of recent runs with pass/fail counts | +| `results/latest_summary.md` | Copy of the latest run summary (backward compatible) | +| `results/latest_summary.json` | Copy of the latest run JSON (backward compatible) | +| `results/suggested_overrides.json` | Copy of the latest suggestion draft (backward compatible) | + +List recent runs: + +```bash +uv run python run_evals.py --list-runs +``` + +Other files may appear under `results/` from optional tooling (audit logs, live description dumps, etc.) — those are not tied to a specific eval run. diff --git a/tests/eval_test/judge/__init__.py b/tests/eval_test/judge/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/eval_test/judge/bedrock_llm.py b/tests/eval_test/judge/bedrock_llm.py new file mode 100644 index 0000000..50ba4e1 --- /dev/null +++ b/tests/eval_test/judge/bedrock_llm.py @@ -0,0 +1,57 @@ +"""DeepEval LLM wrapper backed by AWS Bedrock (Converse API).""" + +from __future__ import annotations + +import json +import os +from typing import Any + +import boto3 +from deepeval.models.base_model import DeepEvalBaseLLM + + +class BedrockLLM(DeepEvalBaseLLM): + def __init__(self, model_id: str | None = None, bedrock_client=None): + self.model_id = model_id or os.environ.get( + "BEDROCK_JUDGE_MODEL_ID", + os.environ.get("BEDROCK_MODEL_ID", "anthropic.claude-3-5-sonnet-20241022-v2:0"), + ) + self._client = bedrock_client or self._build_client() + super().__init__() + + def _build_client(self): + region = os.environ.get("AWS_REGION", "us-east-1") + return boto3.client("bedrock-runtime", region_name=region) + + def load_model(self): + return self._client + + def get_model_name(self) -> str: + return self.model_id + + def _call(self, prompt: str, schema: Any = None) -> str: + response = self._client.converse( + modelId=self.model_id, + messages=[{"role": "user", "content": [{"text": prompt}]}], + ) + text = response["output"]["message"]["content"][0]["text"] + + if schema is not None: + # deepeval passes a Pydantic model as schema for structured output; + # extract the JSON block from the response and parse it. + try: + start = text.index("{") + end = text.rindex("}") + 1 + return schema.model_validate(json.loads(text[start:end])) + except Exception: + pass + + return text + + def generate(self, prompt: str, schema: Any = None) -> str: + return self._call(prompt, schema) + + async def a_generate(self, prompt: str, schema: Any = None) -> str: + import asyncio + loop = asyncio.get_event_loop() + return await loop.run_in_executor(None, self._call, prompt, schema) diff --git a/tests/eval_test/judge/checks.py b/tests/eval_test/judge/checks.py new file mode 100644 index 0000000..59875ad --- /dev/null +++ b/tests/eval_test/judge/checks.py @@ -0,0 +1,114 @@ +"""Deterministic structural checks that run before the LLM judge.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +# Param values compared exactly when non-empty in the expected case. +EXACT_VALUE_KEYS = frozenset( + { + "database_name", + "table_name", + "column_name", + "user_name", + "username", + "role_name", + } +) + +# Param keys that must be present but may differ in value (e.g. SQL wording). +PRESENCE_ONLY_KEYS = frozenset({"sql", "query"}) + + +@dataclass +class ToolCallRecord: + name: str + input_parameters: dict[str, Any] + + +def _check_params(expected_params: dict[str, Any], actual_params: dict[str, Any]) -> list[str]: + errors: list[str] = [] + + for key, expected_value in expected_params.items(): + if key not in actual_params: + errors.append(f"missing param key '{key}'") + continue + + actual_value = actual_params[key] + + if key in PRESENCE_ONLY_KEYS: + if expected_value and not actual_value: + errors.append(f"param '{key}' must not be empty") + continue + + if key in EXACT_VALUE_KEYS and expected_value not in ("", None): + if actual_value != expected_value: + errors.append( + f"param '{key}': expected {expected_value!r}, got {actual_value!r}", + ) + + return errors + + +def _check_tool_pair( + expected: ToolCallRecord, + actual: ToolCallRecord, + *, + label: str, +) -> list[str]: + errors: list[str] = [] + if actual.name != expected.name: + errors.append(f"{label}: expected tool {expected.name!r}, got {actual.name!r}") + errors.extend(_check_params(expected.input_parameters, actual.input_parameters)) + return errors + + +def run_deterministic_checks( + case: dict, + tools_called: list[ToolCallRecord], +) -> list[str]: + """Return a list of structural check failures (empty list means pass).""" + case_type = case.get("type", "happy_path") + expected_raw = case.get("expected_tools", []) + expected = [ + ToolCallRecord(name=t["name"], input_parameters=t.get("params", {})) + for t in expected_raw + ] + + if case_type == "missing_parameter": + if tools_called: + names = [tc.name for tc in tools_called] + return [f"expected no tool calls for missing_parameter case, got {names}"] + return [] + + if not expected: + return [] + + errors: list[str] = [] + + if case_type == "multi_tool": + if len(tools_called) != len(expected): + errors.append( + f"multi_tool: expected {len(expected)} tool call(s), got {len(tools_called)}", + ) + return errors + for i, (exp, act) in enumerate(zip(expected, tools_called, strict=True)): + errors.extend(_check_tool_pair(exp, act, label=f"step {i + 1}")) + return errors + + if not tools_called: + return ["expected at least one tool call, got none"] + + errors.extend(_check_tool_pair(expected[0], tools_called[0], label="primary tool")) + + return errors + + +def assert_deterministic_checks(case: dict, tools_called: list[ToolCallRecord]) -> None: + """Fail fast on structural mismatch before invoking the LLM judge.""" + errors = run_deterministic_checks(case, tools_called) + if errors: + case_id = case.get("id", "") + detail = "; ".join(errors) + raise AssertionError(f"[{case_id}] deterministic check failed: {detail}") diff --git a/tests/eval_test/judge/metrics.py b/tests/eval_test/judge/metrics.py new file mode 100644 index 0000000..3c7fa2b --- /dev/null +++ b/tests/eval_test/judge/metrics.py @@ -0,0 +1,43 @@ +"""deepeval metrics for the Teradata MCP eval suite.""" + +from __future__ import annotations + +from deepeval.metrics import GEval, ToolCorrectnessMetric +from deepeval.test_case import LLMTestCaseParams + + +def tool_correctness_metric(judge_llm) -> ToolCorrectnessMetric: + """Evaluates tool selection accuracy and parameter correctness via LLM judge.""" + return ToolCorrectnessMetric( + threshold=0.5, + model=judge_llm, + include_reason=True, + ) + + +def clarification_metric(judge_llm) -> GEval: + """For missing_parameter cases: checks the agent asked for clarification rather than hallucinating.""" + return GEval( + name="Clarification Check", + criteria=( + "The agent was given a prompt that is missing a required parameter. " + "The agent should ask the user for the missing information rather than " + "inventing a value or proceeding with a guess. " + "Score 1.0 if the response contains a clear request for the missing information. " + "Score 0.0 if the agent fabricated a parameter value or called a tool without asking." + ), + evaluation_params=[ + LLMTestCaseParams.INPUT, + LLMTestCaseParams.ACTUAL_OUTPUT, + ], + model=judge_llm, + threshold=0.5, + ) + + +def get_metrics(case: dict, judge_llm) -> list: + """Return the appropriate metric set for a given test case type.""" + metrics = [tool_correctness_metric(judge_llm)] + if case.get("type") == "missing_parameter": + metrics.append(clarification_metric(judge_llm)) + return metrics diff --git a/tests/eval_test/judge/report.py b/tests/eval_test/judge/report.py new file mode 100644 index 0000000..9b134cd --- /dev/null +++ b/tests/eval_test/judge/report.py @@ -0,0 +1,561 @@ +"""Collect eval outcomes and write human-readable summaries to results/.""" + +from __future__ import annotations + +import json +import os +import re +from dataclasses import asdict, dataclass, field +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +RESULTS_DIR = Path(__file__).resolve().parent.parent / "results" +RUNS_DIR = RESULTS_DIR / "runs" +LATEST_POINTER_FILE = RESULTS_DIR / "latest.json" +INDEX_FILE = RESULTS_DIR / "index.json" +MAX_INDEX_RUNS = 50 + + +@dataclass +class CaseEvalResult: + case_id: str + case_type: str + description: str + input: str + expected_tools: list[dict[str, Any]] + passed: bool + failure_stage: str | None = None + failure_detail: str | None = None + actual_tools: list[dict[str, Any]] | None = None + actual_output: str | None = None + metric_reasons: list[str] = field(default_factory=list) + recommendation: str | None = None + turn_details: list[dict[str, Any]] | None = None + + +@dataclass +class EvalRunReport: + started_at: str + module_filter: str + case_type_filter: str + agent_model_id: str + judge_model_id: str + evals_database: str + description_mode: str = "mcp_server" + description_overrides_file: str | None = None + description_override_count: int = 0 + run_label: str | None = None + results: list[CaseEvalResult] = field(default_factory=list) + + @property + def total(self) -> int: + return len(self.results) + + @property + def passed_count(self) -> int: + return sum(1 for result in self.results if result.passed) + + @property + def failed_count(self) -> int: + return self.total - self.passed_count + + +_report: EvalRunReport | None = None + + +def begin_eval_run( + *, + agent_model_id: str, + judge_model_id: str, + evals_database: str, + description_mode: str = "mcp_server", + description_overrides_file: str | None = None, + description_override_count: int = 0, + run_label: str | None = None, +) -> None: + """Start a fresh in-memory report for the current pytest session.""" + global _report + _report = EvalRunReport( + started_at=datetime.now(timezone.utc).isoformat(), + module_filter=os.environ.get("EVALS_RUN_MODULE", "all"), + case_type_filter=os.environ.get("EVALS_RUN_TYPE", "all"), + agent_model_id=agent_model_id, + judge_model_id=judge_model_id, + evals_database=evals_database, + description_mode=description_mode, + description_overrides_file=description_overrides_file, + description_override_count=description_override_count, + run_label=run_label or os.environ.get("EVALS_RUN_LABEL") or None, + ) + + +def record_case_result(result: CaseEvalResult) -> None: + """Append a case outcome to the current report.""" + if _report is None: + return + _report.results.append(result) + + +def get_current_report() -> EvalRunReport | None: + return _report + + +def _tool_names(tools: list[dict[str, Any]] | None) -> list[str]: + if not tools: + return [] + return [tool["name"] for tool in tools] + + +def _competing_tool_hint(description: str, expected_tool: str) -> str | None: + patterns = [ + rf"\bnot\s+({expected_tool.split('_', 1)[0]}_\w+)\b", + rf"\bover\s+({expected_tool.split('_', 1)[0]}_\w+)\b", + rf"\bvs\.?\s+({expected_tool.split('_', 1)[0]}_\w+)\b", + rf"\binstead of\s+({expected_tool.split('_', 1)[0]}_\w+)\b", + ] + for pattern in patterns: + match = re.search(pattern, description, flags=re.IGNORECASE) + if match: + return match.group(1) + generic = re.search(r"\b(base|dba|sec|qlty|chat|plot|tmpl)_\w+\b", description) + if generic and generic.group(0) != expected_tool: + return generic.group(0) + return None + + +def build_recommendation( + case: dict, + *, + failure_stage: str, + failure_detail: str | None, + expected_tools: list[dict[str, Any]], + actual_tools: list[dict[str, Any]] | None, + metric_reasons: list[str], +) -> str: + """Turn a failure into actionable guidance on prompts and tool descriptions.""" + case_type = case.get("type", "happy_path") + description = case.get("description", "") + prompt = case.get("input") or "(multi-turn — see turn details)" + expected_names = _tool_names(expected_tools) + actual_names = _tool_names(actual_tools) + expected_primary = expected_names[0] if expected_names else "the expected tool" + actual_primary = actual_names[0] if actual_names else "no tool" + + if failure_stage == "agent": + return ( + f"The agent loop failed before scoring: {failure_detail}. " + "Check MCP server connectivity, Bedrock credentials, and that the eval prompt is reachable " + f"with the current environment (`EVALS_DATABASE={os.environ.get('EVALS_DATABASE', '')}`)." + ) + + override_note = "" + if os.environ.get("USE_DESCRIPTION_OVERRIDES", "").lower() in {"1", "true", "yes"} or os.environ.get( + "DESCRIPTION_OVERRIDES_FILE" + ): + override_note = ( + " This run used description_overrides.json rather than live MCP descriptions — " + "if a revised override fixes routing, promote that wording to the MCP server." + ) + + if case_type == "ambiguous_selection": + competing = _competing_tool_hint(description, expected_primary) + competitor_text = ( + f" and `{competing}`" if competing and competing != expected_primary else "" + ) + return ( + f"This case tests routing between `{expected_primary}`{competitor_text}. " + f"Eval prompt: \"{prompt}\". " + f"The agent chose `{actual_primary}` instead of `{expected_primary}`. " + "Recommendation: tighten the MCP tool descriptions so only one tool clearly applies — " + f"sharpen `{expected_primary}` for this scenario or narrow `{actual_primary}` so it does not absorb it. " + "Also confirm the eval prompt uses vocabulary different from both descriptions and is not biased " + f"toward `{actual_primary}`." + f"{override_note}" + ) + + if case_type == "missing_parameter": + if actual_names: + return ( + f"The prompt deliberately omits required information: \"{prompt}\". " + f"The agent called `{', '.join(actual_names)}` instead of asking for clarification. " + "Recommendation: review whether those tool descriptions invite calls without full context, " + "and keep the eval prompt vague enough that asking is the only reasonable response." + ) + return ( + f"The prompt deliberately omits required information: \"{prompt}\". " + "The agent should ask for the missing detail rather than guess or call a tool. " + f"Judge feedback: {'; '.join(metric_reasons) or failure_detail or 'did not request clarification'}. " + "Recommendation: make the missing field obvious in the agent system prompt, or tighten tool descriptions " + "that encourage premature tool use." + ) + + if case_type == "multi_tool": + return ( + f"This workflow case expects tools in order: {', '.join(expected_names)}. " + f"The agent produced: {', '.join(actual_names) or 'no tool calls'}. " + f"Eval prompt: \"{prompt}\". " + f"Failure detail: {failure_detail or '; '.join(metric_reasons)}. " + "Recommendation: verify each tool description mentions when it belongs in a multi-step flow, " + "and confirm the eval prompt states the full task without naming tools directly." + ) + + return ( + f"Expected `{expected_primary}` for prompt: \"{prompt}\". " + f"The agent produced `{actual_primary}`. " + f"Failure detail: {failure_detail or '; '.join(metric_reasons) or 'metric check failed'}. " + "Recommendation: compare the eval prompt with the MCP description for " + f"`{expected_primary}` — the prompt should map clearly to that tool without copying description wording." + f"{override_note}" + ) + + +def _format_tools(tools: list[dict[str, Any]] | None) -> str: + if not tools: + return "_none_" + lines = [] + for tool in tools: + params = json.dumps(tool.get("params", {}), indent=2) + lines.append(f"- `{tool['name']}` with params:\n```json\n{params}\n```") + return "\n".join(lines) + + +def render_markdown(report: EvalRunReport) -> str: + """Render a markdown summary for the eval run.""" + lines = [ + "# Teradata MCP Eval Run Summary", + "", + f"**Started (UTC):** {report.started_at}", + f"**Module filter:** {report.module_filter}", + f"**Case type filter:** {report.case_type_filter}", + f"**Agent model:** {report.agent_model_id}", + f"**Judge model:** {report.judge_model_id}", + f"**Eval database:** {report.evals_database or '(not set)'}", + ] + if report.run_label: + lines.append(f"**Run label:** {report.run_label}") + if report.description_mode == "overrides": + lines.append( + f"**Tool descriptions:** overrides from `{report.description_overrides_file or 'description_overrides.json'}` " + f"({report.description_override_count} tool(s) patched)" + ) + else: + lines.append("**Tool descriptions:** live MCP server (baseline)") + lines.extend( + [ + "", + "## Overview", + "", + "| Metric | Count |", + "| --- | ---: |", + f"| Total cases | {report.total} |", + f"| Passed | {report.passed_count} |", + f"| Failed | {report.failed_count} |", + "", + ] + ) + + failed = [result for result in report.results if not result.passed] + passed = [result for result in report.results if result.passed] + + if failed: + lines.extend(["## Failed cases", ""]) + for result in failed: + lines.extend( + [ + f"### {result.case_id} ({result.case_type})", + "", + f"**Description:** {result.description or '—'}", + "", + "**Eval prompt:**", + "", + f"> {result.input}", + "", + "**Expected tool(s):**", + "", + _format_tools(result.expected_tools), + "", + "**Actual tool(s):**", + "", + _format_tools(result.actual_tools), + "", + ] + ) + if result.actual_output: + lines.extend(["**Agent response (excerpt):**", "", f"> {result.actual_output[:500]}", ""]) + if result.failure_stage or result.failure_detail: + lines.append( + f"**Failure ({result.failure_stage or 'unknown'}):** " + f"{result.failure_detail or '; '.join(result.metric_reasons) or '—'}" + ) + lines.append("") + if result.metric_reasons: + lines.append("**Judge notes:**") + lines.append("") + for reason in result.metric_reasons: + lines.append(f"- {reason}") + lines.append("") + if result.turn_details: + lines.extend(["**Turn details:**", "", "```json", json.dumps(result.turn_details, indent=2), "```", ""]) + if result.recommendation: + lines.extend(["**Recommendation:**", "", result.recommendation, ""]) + else: + lines.extend(["## Failed cases", "", "_None — all cases passed._", ""]) + + lines.extend(["## Passed cases", ""]) + if passed: + for result in passed: + lines.append(f"- `{result.case_id}` ({result.case_type})") + else: + lines.append("_None._") + + lines.append("") + return "\n".join(lines) + + +def _slugify(text: str) -> str: + slug = re.sub(r"[^\w-]+", "-", text.strip().lower()).strip("-") + return slug[:40] if slug else "run" + + +def build_run_id(report: EvalRunReport) -> str: + """Build a human-readable directory name for an eval run.""" + started_at = report.started_at + if started_at.endswith("+00:00"): + started_at = f"{started_at[:-6]}Z" + timestamp = started_at.replace(":", "-") + module = _slugify(report.module_filter or "all") + mode = "overrides" if report.description_mode == "overrides" else "baseline" + parts = [timestamp, module, mode] + if report.case_type_filter and report.case_type_filter != "all": + parts.append(_slugify(report.case_type_filter)) + if report.run_label: + parts.append(_slugify(report.run_label)) + return "__".join(parts) + + +@dataclass +class RunArtifacts: + run_id: str + run_dir: Path + summary_md: Path + summary_json: Path + manifest: Path + + +def load_latest_pointer() -> dict[str, Any] | None: + """Return the latest run pointer document, if present.""" + if not LATEST_POINTER_FILE.exists(): + return None + try: + data = json.loads(LATEST_POINTER_FILE.read_text(encoding="utf-8")) + except Exception: + return None + return data if isinstance(data, dict) else None + + +def resolve_default_summary_path() -> Path: + """Prefer the latest run's summary.json; fall back to legacy latest_summary.json.""" + pointer = load_latest_pointer() + if pointer: + summary_json = pointer.get("summary_json") + if isinstance(summary_json, str): + candidate = RESULTS_DIR / summary_json + if candidate.exists(): + return candidate + legacy = RESULTS_DIR / "latest_summary.json" + if legacy.exists(): + return legacy + return legacy + + +def run_dir_for_summary_path(summary_path: Path) -> Path | None: + """Return the run directory that owns a summary file, when known.""" + resolved = summary_path.resolve() + try: + if resolved.parent.parent == RUNS_DIR.resolve() and resolved.name in {"summary.json", "summary.md"}: + return resolved.parent + except Exception: + pass + + pointer = load_latest_pointer() + if pointer and summary_path.name == "latest_summary.json": + run_dir = pointer.get("run_dir") + if isinstance(run_dir, str): + return RESULTS_DIR / run_dir + return None + + +def resolve_suggestion_output_path(summary_path: Path) -> Path: + """Write suggestion drafts alongside the eval run when possible.""" + run_dir = run_dir_for_summary_path(summary_path) + if run_dir is not None: + return run_dir / "suggested_overrides.json" + return RESULTS_DIR / "suggested_overrides.json" + + +def _summary_payload(report: EvalRunReport) -> dict[str, Any]: + return { + "run_id": build_run_id(report), + "started_at": report.started_at, + "module_filter": report.module_filter, + "case_type_filter": report.case_type_filter, + "run_label": report.run_label, + "agent_model_id": report.agent_model_id, + "judge_model_id": report.judge_model_id, + "evals_database": report.evals_database, + "description_mode": report.description_mode, + "description_overrides_file": report.description_overrides_file, + "description_override_count": report.description_override_count, + "total": report.total, + "passed": report.passed_count, + "failed": report.failed_count, + "cases": [asdict(result) for result in report.results], + } + + +def _manifest_payload(report: EvalRunReport, *, run_id: str) -> dict[str, Any]: + return { + "run_id": run_id, + "started_at": report.started_at, + "module_filter": report.module_filter, + "case_type_filter": report.case_type_filter, + "run_label": report.run_label, + "description_mode": report.description_mode, + "description_overrides_file": report.description_overrides_file, + "description_override_count": report.description_override_count, + "agent_model_id": report.agent_model_id, + "judge_model_id": report.judge_model_id, + "evals_database": report.evals_database, + "total": report.total, + "passed": report.passed_count, + "failed": report.failed_count, + "artifacts": { + "summary_md": "summary.md", + "summary_json": "summary.json", + "manifest": "manifest.json", + "suggested_overrides": "suggested_overrides.json", + }, + } + + +def _index_entry(report: EvalRunReport, *, run_id: str) -> dict[str, Any]: + return { + "run_id": run_id, + "started_at": report.started_at, + "module_filter": report.module_filter, + "case_type_filter": report.case_type_filter, + "run_label": report.run_label, + "description_mode": report.description_mode, + "passed": report.passed_count, + "failed": report.failed_count, + "total": report.total, + "run_dir": f"runs/{run_id}", + "summary_json": f"runs/{run_id}/summary.json", + "summary_md": f"runs/{run_id}/summary.md", + } + + +def _update_run_index(entry: dict[str, Any]) -> None: + existing: list[dict[str, Any]] = [] + if INDEX_FILE.exists(): + try: + data = json.loads(INDEX_FILE.read_text(encoding="utf-8")) + if isinstance(data, dict) and isinstance(data.get("runs"), list): + existing = [item for item in data["runs"] if isinstance(item, dict)] + except Exception: + existing = [] + + run_id = entry.get("run_id") + existing = [item for item in existing if item.get("run_id") != run_id] + runs = [entry, *existing][:MAX_INDEX_RUNS] + INDEX_FILE.write_text(json.dumps({"runs": runs}, indent=2) + "\n", encoding="utf-8") + + +def load_run_index() -> list[dict[str, Any]]: + """Return indexed eval runs, newest first.""" + if not INDEX_FILE.exists(): + return [] + try: + data = json.loads(INDEX_FILE.read_text(encoding="utf-8")) + if isinstance(data, dict) and isinstance(data.get("runs"), list): + return [item for item in data["runs"] if isinstance(item, dict)] + except Exception: + pass + return [] + + +def format_run_index(limit: int = 20) -> str: + """Render a compact table of recent eval runs.""" + runs = load_run_index()[:limit] + if not runs: + return "No indexed eval runs yet." + + lines = [ + "Recent eval runs (newest first):", + "", + f"{'Run ID':<56} {'Mode':<10} {'Pass':>4} {'Fail':>4}", + f"{'-' * 56} {'-' * 10} {'-' * 4} {'-' * 4}", + ] + for run in runs: + run_id = str(run.get("run_id", "?"))[:56] + mode = str(run.get("description_mode", "?"))[:10] + passed = run.get("passed", "?") + failed = run.get("failed", "?") + lines.append(f"{run_id:<56} {mode:<10} {passed:>4} {failed:>4}") + lines.extend(["", f"Latest pointer: {LATEST_POINTER_FILE}", f"Full index: {INDEX_FILE}"]) + return "\n".join(lines) + + +def write_eval_summary(report: EvalRunReport) -> RunArtifacts: + """Write run artifacts under results/runs// and update index pointers.""" + RESULTS_DIR.mkdir(parents=True, exist_ok=True) + RUNS_DIR.mkdir(parents=True, exist_ok=True) + + run_id = build_run_id(report) + run_dir = RUNS_DIR / run_id + run_dir.mkdir(parents=True, exist_ok=True) + + summary_md = run_dir / "summary.md" + summary_json = run_dir / "summary.json" + manifest = run_dir / "manifest.json" + latest_markdown = RESULTS_DIR / "latest_summary.md" + latest_json = RESULTS_DIR / "latest_summary.json" + + markdown = render_markdown(report) + payload = _summary_payload(report) + manifest_payload = _manifest_payload(report, run_id=run_id) + + summary_md.write_text(markdown + "\n", encoding="utf-8") + summary_json.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") + manifest.write_text(json.dumps(manifest_payload, indent=2) + "\n", encoding="utf-8") + + latest_markdown.write_text(markdown + "\n", encoding="utf-8") + latest_json.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") + + run_dir_rel = f"runs/{run_id}" + pointer = { + "run_id": run_id, + "started_at": report.started_at, + "run_dir": run_dir_rel, + "summary_md": f"{run_dir_rel}/summary.md", + "summary_json": f"{run_dir_rel}/summary.json", + "manifest": f"{run_dir_rel}/manifest.json", + "module_filter": report.module_filter, + "case_type_filter": report.case_type_filter, + "run_label": report.run_label, + "description_mode": report.description_mode, + "passed": report.passed_count, + "failed": report.failed_count, + "total": report.total, + } + LATEST_POINTER_FILE.write_text(json.dumps(pointer, indent=2) + "\n", encoding="utf-8") + _update_run_index(_index_entry(report, run_id=run_id)) + + return RunArtifacts( + run_id=run_id, + run_dir=run_dir, + summary_md=summary_md, + summary_json=summary_json, + manifest=manifest, + ) diff --git a/tests/eval_test/judge/suggest_overrides.py b/tests/eval_test/judge/suggest_overrides.py new file mode 100644 index 0000000..cc48aa3 --- /dev/null +++ b/tests/eval_test/judge/suggest_overrides.py @@ -0,0 +1,657 @@ +"""LLM-assisted draft descriptions for description_overrides.json from eval failures.""" + +from __future__ import annotations + +import asyncio +import json +import os +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +from dotenv import load_dotenv +from mcp import ClientSession +from mcp.client.streamable_http import streamablehttp_client + +from judge.report import ( + RESULTS_DIR, + _competing_tool_hint, + resolve_default_summary_path, + resolve_suggestion_output_path, + run_dir_for_summary_path, +) + +load_dotenv() + +DEFAULT_SUMMARY_PATH = RESULTS_DIR / "latest_summary.json" +DEFAULT_OUTPUT_PATH = RESULTS_DIR / "suggested_overrides.json" + +DEFAULT_CASE_TYPES = frozenset({"ambiguous_selection", "happy_path", "missing_parameter", "multi_tool"}) + +AMBIGUOUS_PROMPT = """\ +You are improving MCP tool descriptions so an AI agent routes user requests to the correct tool. + +An ambiguous_selection eval failed: the agent chose the wrong tool. + +Eval case id: {case_id} +Case intent: {case_description} +User prompt: {eval_prompt} +Expected tool: {expected_tool} +Tool the agent chose instead: {actual_tool} +Failure: {failure_detail} +{judge_notes_block} + +Current MCP descriptions: + +[{expected_tool}] +{expected_description} + +[{actual_tool}] +{actual_description} + +{existing_override_block} + +Rewrite BOTH descriptions so only `{expected_tool}` fits the prompt and `{actual_tool}` clearly does not. +Rules: state when to use and explicit "Do NOT use when…" limits; stay faithful to Teradata MCP behavior; +2–4 sentences each; do not copy the eval prompt verbatim. + +Respond with valid JSON only: +{{ + "tools": {{ + "{expected_tool}": "", + "{actual_tool}": "" + }}, + "rationale": "" +}} +""" + +HAPPY_PATH_PROMPT = """\ +You are improving MCP tool descriptions so an AI agent selects the correct tool and parameters. + +A happy_path eval failed: the agent did not call the expected tool correctly. + +Eval case id: {case_id} +Case intent: {case_description} +User prompt: {eval_prompt} +Expected tool: {expected_tool} +Actual tool(s) called: {actual_tools_summary} +Failure: {failure_detail} +{judge_notes_block} + +Current MCP descriptions: +{tool_description_block} + +{existing_override_block} + +Revise the description(s) for: {tool_list}. +Make `{expected_tool}` clearly match prompts like the one above. If another tool absorbed the request, \ +narrow that tool with explicit limitations. +Rules: stay faithful to Teradata MCP behavior; 2–4 sentences per tool; do not copy the eval prompt verbatim. + +Respond with valid JSON only: +{{ + "tools": {{ + "": "" + }}, + "rationale": "" +}} +Include one entry per tool listed in "Revise the description(s) for". +""" + +MISSING_PARAMETER_PROMPT = """\ +You are improving MCP tool descriptions so an AI agent asks for missing information instead of \ +calling tools prematurely or guessing. + +A missing_parameter eval failed. + +Eval case id: {case_id} +Case intent: {case_description} +User prompt: {eval_prompt} +Expected behavior: ask for clarification — do NOT call a tool yet +Actual tool(s) called: {actual_tools_summary} +Failure: {failure_detail} +{judge_notes_block} +{turn_details_block} + +Current MCP descriptions: +{tool_description_block} + +{existing_override_block} + +Revise the description(s) for: {tool_list}. +Each revised description must state which required inputs must be present before calling \ +(e.g. database_name, table_name, sql) and tell the agent to ask the user when they are missing. +Rules: stay faithful to Teradata MCP behavior; 2–4 sentences per tool; do not copy the eval prompt verbatim. + +Respond with valid JSON only: +{{ + "tools": {{ + "": "" + }}, + "rationale": "" +}} +""" + +MULTI_TOOL_PROMPT = """\ +You are improving MCP tool descriptions for multi-step workflows. + +A multi_tool eval failed: the agent did not chain tools in the expected order. + +Eval case id: {case_id} +Case intent: {case_description} +User prompt: {eval_prompt} +Expected tool order: {expected_tools_summary} +Actual tool(s) called: {actual_tools_summary} +Failure: {failure_detail} +{judge_notes_block} + +Current MCP descriptions: +{tool_description_block} + +{existing_override_block} + +Revise the description(s) for: {tool_list}. +Clarify when each tool applies in a multi-step task and what must be completed before the next tool. +Rules: stay faithful to Teradata MCP behavior; 2–4 sentences per tool; do not copy the eval prompt verbatim. + +Respond with valid JSON only: +{{ + "tools": {{ + "": "" + }}, + "rationale": "" +}} +""" + + +@dataclass +class CaseSuggestionPlan: + case_id: str + case_type: str + tool_names: list[str] + prompt: str + skip_reason: str | None = None + + +def load_summary(path: Path) -> dict[str, Any]: + if not path.exists(): + raise FileNotFoundError(f"Summary not found: {path}. Run baseline evals first.") + return json.loads(path.read_text()) + + +def load_failed_cases( + summary: dict[str, Any], + *, + case_types: frozenset[str] | None = None, +) -> list[dict[str, Any]]: + """Return failed cases from an eval summary, optionally filtered by case type.""" + allowed = case_types or DEFAULT_CASE_TYPES + cases = summary.get("cases", []) + return [ + case + for case in cases + if not case.get("passed", True) and case.get("case_type") in allowed + ] + + +def load_failed_ambiguous_cases(summary: dict[str, Any]) -> list[dict[str, Any]]: + """Return failed ambiguous_selection cases (backward-compatible helper).""" + return load_failed_cases(summary, case_types=frozenset({"ambiguous_selection"})) + + +def _tool_name(blocks: list[dict[str, Any]] | None, *, fallback: str = "") -> str: + if not blocks: + return fallback + return blocks[0].get("name", fallback) + + +def _tool_names_from_blocks(blocks: list[dict[str, Any]] | None) -> list[str]: + if not blocks: + return [] + return [tool["name"] for tool in blocks if tool.get("name")] + + +def tool_pair_for_case(case: dict[str, Any]) -> tuple[str, str] | None: + """Return (expected_tool, competing_tool) for an ambiguous_selection failure.""" + expected = _tool_name(case.get("expected_tools")) + if not expected: + return None + + actual = _tool_name(case.get("actual_tools")) + if actual and actual != expected: + return expected, actual + + competing = _competing_tool_hint(case.get("description", ""), expected) + if competing and competing != expected: + return expected, competing + + if actual: + return expected, actual + + return None + + +def _format_tool_list(names: list[str]) -> str: + return ", ".join(f"`{name}`" for name in names) if names else "(none)" + + +def _format_tools_summary(blocks: list[dict[str, Any]] | None) -> str: + if not blocks: + return "none" + parts = [] + for tool in blocks: + params = json.dumps(tool.get("params", {}), sort_keys=True) + parts.append(f"{tool['name']}({params})") + return "; ".join(parts) + + +def _existing_override_block(tool_names: list[str], existing_overrides: dict[str, str]) -> str: + if not existing_overrides: + return "" + lines = ["Existing local overrides (may be empty for these tools):"] + for name in tool_names: + if name in existing_overrides: + lines.append(f"[{name}] {existing_overrides[name]}") + if len(lines) == 1: + return "" + return "\n".join(lines) + + +def _judge_notes_block(case: dict[str, Any]) -> str: + reasons = case.get("metric_reasons") or [] + if not reasons: + return "" + return "Judge notes:\n" + "\n".join(f"- {reason}" for reason in reasons) + + +def _turn_details_block(case: dict[str, Any]) -> str: + turn_details = case.get("turn_details") + if not turn_details: + return "" + return "Turn details:\n" + json.dumps(turn_details, indent=2) + + +def _tool_description_block(tool_names: list[str], live_descriptions: dict[str, str]) -> str: + blocks = [] + for name in tool_names: + blocks.append(f"[{name}]\n{live_descriptions.get(name, '(no description on MCP server)')}") + return "\n\n".join(blocks) + + +def tools_for_case(case: dict[str, Any]) -> list[str]: + """Return tool names whose descriptions should be revised for this failure.""" + case_type = case.get("case_type", "happy_path") + expected = _tool_names_from_blocks(case.get("expected_tools")) + actual = _tool_names_from_blocks(case.get("actual_tools")) + + if case_type == "ambiguous_selection": + pair = tool_pair_for_case(case) + return list(pair) if pair else [] + + if case_type == "happy_path": + names = list(dict.fromkeys(expected + [n for n in actual if n not in expected])) + return names or expected + + if case_type == "missing_parameter": + if actual: + return actual + # Clarification-only failure with no tool calls — nothing to patch in descriptions. + return [] + + if case_type == "multi_tool": + return list(dict.fromkeys(expected + [n for n in actual if n not in expected])) + + return [] + + +def build_case_plan( + case: dict[str, Any], + *, + live_descriptions: dict[str, str], + existing_overrides: dict[str, str], +) -> CaseSuggestionPlan | None: + """Build the Bedrock prompt plan for one failed case, or None if not actionable.""" + case_id = case.get("case_id", "") + case_type = case.get("case_type", "happy_path") + tool_names = tools_for_case(case) + + if case_type == "missing_parameter" and not tool_names: + return CaseSuggestionPlan( + case_id=case_id, + case_type=case_type, + tool_names=[], + prompt="", + skip_reason="No tool calls to address — failure is clarification wording, not tool routing.", + ) + + if not tool_names: + return None + + common = { + "case_id": case_id, + "case_description": case.get("description", ""), + "eval_prompt": case.get("input", ""), + "failure_detail": case.get("failure_detail") or "; ".join(case.get("metric_reasons") or []) or "unknown", + "judge_notes_block": _judge_notes_block(case), + "existing_override_block": _existing_override_block(tool_names, existing_overrides), + "tool_description_block": _tool_description_block(tool_names, live_descriptions), + "tool_list": _format_tool_list(tool_names), + } + + if case_type == "ambiguous_selection": + expected_tool, actual_tool = tool_names[0], tool_names[1] + prompt = AMBIGUOUS_PROMPT.format( + expected_tool=expected_tool, + actual_tool=actual_tool, + expected_description=live_descriptions.get(expected_tool, "(no description on MCP server)"), + actual_description=live_descriptions.get(actual_tool, "(no description on MCP server)"), + **common, + ) + elif case_type == "happy_path": + expected_tool = _tool_name(case.get("expected_tools")) + prompt = HAPPY_PATH_PROMPT.format( + expected_tool=expected_tool, + actual_tools_summary=_format_tools_summary(case.get("actual_tools")), + **common, + ) + elif case_type == "missing_parameter": + prompt = MISSING_PARAMETER_PROMPT.format( + actual_tools_summary=_format_tools_summary(case.get("actual_tools")), + turn_details_block=_turn_details_block(case), + **common, + ) + elif case_type == "multi_tool": + prompt = MULTI_TOOL_PROMPT.format( + expected_tools_summary=_format_tools_summary(case.get("expected_tools")), + actual_tools_summary=_format_tools_summary(case.get("actual_tools")), + **common, + ) + else: + return None + + return CaseSuggestionPlan( + case_id=case_id, + case_type=case_type, + tool_names=tool_names, + prompt=prompt, + ) + + +def collect_tool_names(cases: list[dict[str, Any]]) -> set[str]: + names: set[str] = set() + for case in cases: + names.update(tools_for_case(case)) + return names + + +async def fetch_live_descriptions(mcp_url: str, tool_names: set[str]) -> dict[str, str]: + """Fetch live MCP descriptions for the requested tool names.""" + if not tool_names: + return {} + + async with streamablehttp_client(mcp_url) as (read, write, _): + async with ClientSession(read, write) as session: + await session.initialize() + result = await session.list_tools() + + descriptions: dict[str, str] = {} + for tool in result.tools: + if tool.name in tool_names: + descriptions[tool.name] = tool.description or "" + return descriptions + + +def load_existing_overrides(path: Path | None) -> dict[str, str]: + if path is None or not path.exists(): + return {} + try: + data = json.loads(path.read_text()) + if isinstance(data, dict): + return {k: v for k, v in data.items() if isinstance(v, str) and not k.startswith("_")} + except Exception: + pass + return {} + + +def parse_suggestion_response(text: str) -> dict[str, Any]: + """Extract the JSON object from an LLM response.""" + start = text.index("{") + end = text.rindex("}") + 1 + parsed = json.loads(text[start:end]) + if not isinstance(parsed, dict) or "tools" not in parsed: + raise ValueError("LLM response missing 'tools' object") + return parsed + + +def suggest_for_plan( + plan: CaseSuggestionPlan, + *, + bedrock_client, + model_id: str, +) -> dict[str, Any]: + response = bedrock_client.converse( + modelId=model_id, + messages=[{"role": "user", "content": [{"text": plan.prompt}]}], + ) + text = response["output"]["message"]["content"][0]["text"].strip() + return parse_suggestion_response(text) + + +def merge_suggestions( + *, + source_summary: Path, + case_results: list[dict[str, Any]], + skipped: list[dict[str, str]] | None = None, +) -> dict[str, Any]: + """Build the output document written to suggested_overrides.json.""" + merged_tools: dict[str, str] = {} + for entry in case_results: + for name, description in entry.get("tools", {}).items(): + merged_tools[name] = description + + payload: dict[str, Any] = { + "_comment": "LLM draft — review before running suggest_overrides.py --apply", + "_generated_at": datetime.now(timezone.utc).isoformat(), + "_source_summary": str(source_summary), + "_supported_case_types": sorted(DEFAULT_CASE_TYPES), + "_instructions": ( + "Review this draft, then run: uv run python suggest_overrides.py --apply " + "and re-run evals with: uv run python run_evals.py --with-description-overrides" + ), + "suggestions": merged_tools, + "cases": case_results, + } + run_dir = run_dir_for_summary_path(source_summary) + if run_dir is not None: + payload["_source_run_id"] = run_dir.name + payload["_source_run_dir"] = str(run_dir.relative_to(RESULTS_DIR)) + if skipped: + payload["skipped"] = skipped + return payload + + +DEFAULT_OVERRIDES_COMMENT = ( + "Dev-space description overrides. Gitignored. Edit freely, then promote to MCP server." +) + + +def load_suggestion_draft(path: Path) -> dict[str, str]: + """Return tool-name → description entries from a suggested_overrides.json draft.""" + if not path.exists(): + raise FileNotFoundError(f"Suggestions file not found: {path}") + + data = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(data, dict): + raise ValueError(f"Invalid suggestions file (expected JSON object): {path}") + + suggestions = data.get("suggestions", {}) + if not isinstance(suggestions, dict): + raise ValueError(f"Invalid suggestions file (missing 'suggestions' object): {path}") + + return {name: text for name, text in suggestions.items() if isinstance(name, str) and isinstance(text, str)} + + +def apply_suggestions_to_overrides( + *, + suggestions_path: Path | None = None, + overrides_path: Path | None = None, + only_tools: set[str] | None = None, + dry_run: bool = False, +) -> dict[str, Any]: + """Replace description_overrides.json with reviewed suggestions only.""" + from agent.client import DEFAULT_OVERRIDES_FILE + + resolved_suggestions = suggestions_path or resolve_suggestion_output_path(resolve_default_summary_path()) + target = overrides_path or DEFAULT_OVERRIDES_FILE + suggestions = load_suggestion_draft(resolved_suggestions) + if only_tools is not None: + suggestions = {name: text for name, text in suggestions.items() if name in only_tools} + + if not suggestions: + return { + "message": "No suggestions to apply.", + "suggestions_path": str(resolved_suggestions), + "overrides_path": str(target), + "applied": [], + "removed": [], + } + + previous_tools: set[str] = set() + comment = DEFAULT_OVERRIDES_COMMENT + if target.exists(): + previous_raw = json.loads(target.read_text(encoding="utf-8")) + if not isinstance(previous_raw, dict): + raise ValueError(f"Invalid overrides file (expected JSON object): {target}") + previous_tools = { + key for key, value in previous_raw.items() if isinstance(value, str) and not key.startswith("_") + } + if isinstance(previous_raw.get("_comment"), str): + comment = previous_raw["_comment"] + + applied = sorted(suggestions) + removed = sorted(previous_tools - set(suggestions)) + raw = {"_comment": comment, **suggestions} + + if not dry_run: + target.parent.mkdir(parents=True, exist_ok=True) + target.write_text(json.dumps(raw, indent=2) + "\n", encoding="utf-8") + + return { + "suggestions_path": str(resolved_suggestions), + "overrides_path": str(target), + "applied": applied, + "removed": removed, + "applied_count": len(suggestions), + } + + +def _parse_case_types(case_type: str | None) -> frozenset[str]: + if not case_type: + return DEFAULT_CASE_TYPES + selected = {part.strip() for part in case_type.split(",") if part.strip()} + unknown = selected - DEFAULT_CASE_TYPES + if unknown: + allowed = ", ".join(sorted(DEFAULT_CASE_TYPES)) + raise ValueError(f"Unknown case type(s): {', '.join(sorted(unknown))}. Allowed: {allowed}") + return frozenset(selected) + + +def generate_suggestions( + *, + summary_path: Path | None = None, + output_path: Path | None = None, + overrides_path: Path | None = None, + mcp_url: str | None = None, + model_id: str | None = None, + case_type: str | None = None, + dry_run: bool = False, +) -> dict[str, Any]: + """Generate draft override descriptions for failed eval cases.""" + import boto3 + + resolved_summary = summary_path or resolve_default_summary_path() + resolved_output = output_path or resolve_suggestion_output_path(resolved_summary) + + summary = load_summary(resolved_summary) + allowed_types = _parse_case_types(case_type) + failed_cases = load_failed_cases(summary, case_types=allowed_types) + if not failed_cases: + allowed = ", ".join(sorted(allowed_types)) + return { + "suggestions": {}, + "cases": [], + "message": f"No failed cases in summary for type(s): {allowed}.", + } + + tool_names = collect_tool_names(failed_cases) + resolved_url = mcp_url or os.environ.get("MCP_SERVER_URL", "http://127.0.0.1:8001/mcp") + live_descriptions = asyncio.run(fetch_live_descriptions(resolved_url, tool_names)) + + missing = sorted(name for name in tool_names if name not in live_descriptions) + if missing: + raise RuntimeError(f"Could not fetch live descriptions for: {', '.join(missing)}") + + existing_overrides = load_existing_overrides(overrides_path) + resolved_model = model_id or os.environ.get( + "BEDROCK_JUDGE_MODEL_ID", + os.environ.get("BEDROCK_MODEL_ID", "anthropic.claude-3-5-sonnet-20241022-v2:0"), + ) + region = os.environ.get("AWS_REGION", "us-east-1") + bedrock_client = boto3.client("bedrock-runtime", region_name=region) + + case_results: list[dict[str, Any]] = [] + skipped: list[dict[str, str]] = [] + + for case in failed_cases: + plan = build_case_plan(case, live_descriptions=live_descriptions, existing_overrides=existing_overrides) + if plan is None: + continue + if plan.skip_reason: + skipped.append({"case_id": plan.case_id, "case_type": plan.case_type, "reason": plan.skip_reason}) + continue + + if dry_run: + case_results.append( + { + "case_id": plan.case_id, + "case_type": plan.case_type, + "tool_names": plan.tool_names, + "eval_prompt": case.get("input"), + "prompt_preview": plan.prompt, + } + ) + continue + + parsed = suggest_for_plan(plan, bedrock_client=bedrock_client, model_id=resolved_model) + tools = parsed.get("tools", {}) + if not isinstance(tools, dict): + raise ValueError(f"Invalid tools payload for case {plan.case_id}") + + case_results.append( + { + "case_id": plan.case_id, + "case_type": plan.case_type, + "tool_names": plan.tool_names, + "eval_prompt": case.get("input"), + "tools": {k: v for k, v in tools.items() if isinstance(v, str)}, + "rationale": parsed.get("rationale", ""), + } + ) + + if not case_results and not skipped: + return { + "suggestions": {}, + "cases": [], + "message": "Failed cases found but none were actionable for description overrides.", + } + + payload = merge_suggestions(source_summary=resolved_summary, case_results=case_results, skipped=skipped or None) + if not dry_run and (case_results or skipped): + resolved_output.parent.mkdir(parents=True, exist_ok=True) + resolved_output.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") + legacy_output = RESULTS_DIR / "suggested_overrides.json" + if resolved_output != legacy_output: + legacy_output.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") + + return payload diff --git a/tests/eval_test/preflight.py b/tests/eval_test/preflight.py new file mode 100644 index 0000000..ae94ac7 --- /dev/null +++ b/tests/eval_test/preflight.py @@ -0,0 +1,74 @@ +"""Verify eval prerequisites before running live evals against Teradata.""" + +from __future__ import annotations + +import os +import sys + +from dotenv import load_dotenv + +load_dotenv() + +EVAL_TABLES = ("evals_employees", "evals_orders") + + +def check_eval_tables() -> list[str]: + """Return human-readable errors; empty list means OK.""" + db = os.environ.get("EVALS_DATABASE", "").strip() + if not db: + return ["EVALS_DATABASE is not set — copy .env.example to .env and fill in values"] + + missing_env = [ + name + for name in ("TERADATA_HOST", "TERADATA_USER", "TERADATA_PASSWORD") + if not os.environ.get(name, "").strip() + ] + if missing_env: + return [f"Missing Teradata env var(s): {', '.join(missing_env)}"] + + try: + from setup_test_data import get_connection + except ImportError as exc: + return [f"Cannot import Teradata helpers: {exc}"] + + errors: list[str] = [] + try: + with get_connection() as con: + with con.cursor() as cur: + for table in EVAL_TABLES: + try: + cur.execute(f"SELECT COUNT(*) FROM {db}.{table}") + row_count = cur.fetchone()[0] + except Exception as exc: + errors.append(f"{db}.{table} is not accessible: {exc}") + continue + if row_count == 0: + errors.append(f"{db}.{table} exists but has no rows") + except Exception as exc: + errors.append(f"Cannot connect to Teradata ({os.environ.get('TERADATA_HOST')}): {exc}") + + if errors: + errors.append("Fix with: uv run python setup_test_data.py") + + return errors + + +def run_preflight() -> None: + errors = check_eval_tables() + if not errors: + db = os.environ.get("EVALS_DATABASE", "").strip() + print(f"Preflight OK — {db}.evals_employees and {db}.evals_orders are ready.") + return + + print("Preflight failed:", file=sys.stderr) + for error in errors: + print(f" - {error}", file=sys.stderr) + raise SystemExit(1) + + +def main() -> None: + run_preflight() + + +if __name__ == "__main__": + main() diff --git a/tests/eval_test/pyproject.toml b/tests/eval_test/pyproject.toml new file mode 100644 index 0000000..d632039 --- /dev/null +++ b/tests/eval_test/pyproject.toml @@ -0,0 +1,31 @@ +[project] +name = "teradata-mcp-evals" +version = "0.1.0" +description = "Eval suite for the Teradata MCP Server community edition" +requires-python = ">=3.11" +dependencies = [ + "deepeval>=1.4.0", + "mcp>=1.0.0", + "boto3>=1.34.0", + "python-dotenv>=1.0.0", + "pytest>=8.0.0", + "pytest-asyncio>=0.23.0", + "teradatasql>=20.0.0", +] + +[project.scripts] +run-evals = "run_evals:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["agent", "judge"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +testpaths = ["tests"] + +[tool.ruff] +line-length = 120 diff --git a/tests/eval_test/run_evals.py b/tests/eval_test/run_evals.py new file mode 100644 index 0000000..5828b5d --- /dev/null +++ b/tests/eval_test/run_evals.py @@ -0,0 +1,136 @@ +""" +Entry point for the Teradata MCP eval suite. + +Usage: + uv run python run_evals.py # all modules + uv run python run_evals.py --module base # one module + uv run python run_evals.py --module base --type ambiguous_selection + uv run python run_evals.py --verbose + uv run python run_evals.py --module base --with-description-overrides +""" + +from __future__ import annotations + +import argparse +import os +import subprocess +import sys + +from preflight import run_preflight +from agent.client import description_overrides_enabled, resolve_description_overrides_file +from judge.report import format_run_index, load_latest_pointer + + +CASE_TYPE_FILTERS = { + "happy_path": "happy", + "ambiguous_selection": "ambiguous", + "missing_parameter": "missing", + "multi_tool": "multi_tool", + "multi_turn": "clarify_then_call", +} + + +def main() -> None: + parser = argparse.ArgumentParser(description="Run Teradata MCP evals via deepeval + pytest") + parser.add_argument("--module", help="Run evals for a specific module only (base, sec, dba, ...)") + parser.add_argument( + "--type", + dest="case_type", + help=( + "Filter by case type (happy_path, ambiguous_selection, missing_parameter, " + "multi_tool, multi_turn). Matches substrings in pytest case IDs." + ), + ) + parser.add_argument("--verbose", "-v", action="store_true", help="Verbose pytest output") + parser.add_argument( + "--skip-preflight", + action="store_true", + help="Skip Teradata eval-table check (not recommended for live eval runs)", + ) + parser.add_argument( + "--with-description-overrides", + action="store_true", + help=( + "Patch tool descriptions from description_overrides.json before routing " + "(default: use live MCP server descriptions as baseline)" + ), + ) + parser.add_argument( + "--description-overrides-file", + help="Path to overrides JSON (enables overrides; default: description_overrides.json)", + ) + parser.add_argument( + "--run-label", + help="Optional label appended to the run directory name (e.g. after-tablelist-fix)", + ) + parser.add_argument( + "--list-runs", + action="store_true", + help="List recent eval runs from results/index.json and exit", + ) + args = parser.parse_args() + + if args.list_runs: + print(format_run_index()) + pointer = load_latest_pointer() + if pointer: + print("") + print(f"Latest run: {pointer.get('run_id')}") + print(f" dir: results/{pointer.get('run_dir')}") + print(f" summary: results/{pointer.get('summary_md')}") + sys.exit(0) + + if not args.skip_preflight: + run_preflight() + + os.environ["EVALS_RUN_MODULE"] = args.module or "all" + os.environ["EVALS_RUN_TYPE"] = args.case_type or "all" + if args.run_label: + os.environ["EVALS_RUN_LABEL"] = args.run_label + elif "EVALS_RUN_LABEL" in os.environ: + del os.environ["EVALS_RUN_LABEL"] + + if args.with_description_overrides or args.description_overrides_file: + os.environ["USE_DESCRIPTION_OVERRIDES"] = "1" + if args.description_overrides_file: + os.environ["DESCRIPTION_OVERRIDES_FILE"] = args.description_overrides_file + + cmd = ["deepeval", "test", "run", "tests/"] + + if args.module: + cmd += ["-k", f"test_{args.module}"] + + if args.case_type: + keyword = CASE_TYPE_FILTERS.get(args.case_type, args.case_type) + existing_k = next((cmd[i + 1] for i, c in enumerate(cmd) if c == "-k"), None) + if existing_k: + idx = cmd.index("-k") + cmd[idx + 1] = f"{existing_k} and {keyword}" + else: + cmd += ["-k", keyword] + + if args.verbose: + cmd.append("-v") + + if description_overrides_enabled(): + overrides_file = resolve_description_overrides_file() + print(f"Tool descriptions: overrides from {overrides_file}") + else: + print("Tool descriptions: live MCP server (baseline)") + + print(f"Running: {' '.join(cmd)}\n") + result = subprocess.run(cmd) + if result.returncode in {0, 1}: + pointer = load_latest_pointer() + if pointer: + print("") + print(f"Eval run: {pointer.get('run_id')}") + print(f"Run directory: results/{pointer.get('run_dir')}") + print(f"Summary: results/{pointer.get('summary_md')}") + print("Latest copy: results/latest_summary.md") + print("All runs: uv run python run_evals.py --list-runs") + sys.exit(result.returncode) + + +if __name__ == "__main__": + main() diff --git a/tests/eval_test/setup_test_data.py b/tests/eval_test/setup_test_data.py new file mode 100644 index 0000000..d0a2058 --- /dev/null +++ b/tests/eval_test/setup_test_data.py @@ -0,0 +1,132 @@ +""" +Creates the two eval tables in EVALS_DATABASE and populates them with sample data. + +evals_employees — used by: base, qlty (stats), plot (radar) +evals_orders — used by: base, qlty (missing/negative values), plot (line/pie/polar) + +Usage: + python setup_test_data.py + python setup_test_data.py --drop-first # drop and recreate if tables already exist +""" + +from __future__ import annotations + +import argparse +import os + +import teradatasql +from dotenv import load_dotenv + +load_dotenv() + +CREATE_EMPLOYEES = """ +CREATE TABLE {db}.evals_employees ( + employee_id INTEGER NOT NULL, + name VARCHAR(100) NOT NULL, + department VARCHAR(50) NOT NULL, + salary DECIMAL(10,2), + region VARCHAR(50) NOT NULL, + hire_date DATE NOT NULL, + manager_id INTEGER, + PRIMARY KEY (employee_id) +) +""" + +CREATE_ORDERS = """ +CREATE TABLE {db}.evals_orders ( + order_id INTEGER NOT NULL, + customer_name VARCHAR(100) NOT NULL, + order_date DATE NOT NULL, + ship_date DATE, + amount DECIMAL(10,2) NOT NULL, + product_category VARCHAR(50) NOT NULL, + quantity INTEGER NOT NULL, + PRIMARY KEY (order_id) +) +""" + +EMPLOYEES_DATA = [ + (1, "Alice Johnson", "Engineering", 95000.00, "North", "2019-03-15", None), + (2, "Bob Smith", "Sales", 72000.00, "South", "2020-07-01", 1), + (3, "Carol White", "HR", 68000.00, "East", "2018-11-20", 1), + (4, "David Brown", "Finance", 85000.00, "West", "2021-01-10", 1), + (5, "Eva Martinez", "Engineering", 102000.00,"North", "2017-06-05", 1), + (6, "Frank Lee", "Sales", 69500.00, "South", "2022-04-18", 2), + (7, "Grace Kim", "Marketing", 77000.00, "East", "2020-09-30", 1), + (8, "Henry Adams", "Finance", 91000.00, "West", "2019-12-01", 4), + (9, "Isla Thompson", "HR", None, "North", "2023-02-14", 3), + (10, "Jack Wilson", "Engineering", 88000.00, "South", "2016-08-22", 5), +] + +ORDERS_DATA = [ + (1001, "Acme Corp", "2024-01-05", "2024-01-08", 1250.00, "Electronics", 3), + (1002, "Beta Ltd", "2024-01-12", "2024-01-15", -200.00, "Clothing", 1), # refund + (1003, "Gamma Inc", "2024-02-03", None, 875.50, "Food", 5), # not shipped + (1004, "Delta Co", "2024-02-17", "2024-02-20", 3400.00, "Electronics", 2), + (1005, "Epsilon LLC", "2024-03-01", "2024-03-04", 560.00, "Books", 8), + (1006, "Zeta Partners", "2024-03-15", None, -150.00, "Clothing", 1), # refund, not shipped + (1007, "Eta Group", "2024-04-02", "2024-04-06", 2100.00, "Sports", 4), + (1008, "Theta Corp", "2024-04-20", "2024-04-23", 930.00, "Food", 6), + (1009, "Iota Inc", "2024-05-08", None, 1780.00, "Electronics", 1), # not shipped + (1010, "Kappa Ltd", "2024-05-25", "2024-05-28", 4200.00, "Sports", 7), +] + + +def get_connection(): + host = os.environ["TERADATA_HOST"] + user = os.environ["TERADATA_USER"] + password = os.environ["TERADATA_PASSWORD"] + return teradatasql.connect(host=host, user=user, password=password) + + +def drop_tables(cursor, db: str) -> None: + for table in ("evals_employees", "evals_orders"): + try: + cursor.execute(f"DROP TABLE {db}.{table}") + print(f" Dropped {db}.{table}") + except Exception: + pass # table didn't exist + + +def create_and_populate(cursor, db: str) -> None: + print(f"Creating {db}.evals_employees ...") + cursor.execute(CREATE_EMPLOYEES.format(db=db)) + + for row in EMPLOYEES_DATA: + cursor.execute( + f"INSERT INTO {db}.evals_employees VALUES (?,?,?,?,?,?,?)", [list(row)] + ) + print(f" Inserted {len(EMPLOYEES_DATA)} rows") + + print(f"Creating {db}.evals_orders ...") + cursor.execute(CREATE_ORDERS.format(db=db)) + + for row in ORDERS_DATA: + cursor.execute( + f"INSERT INTO {db}.evals_orders VALUES (?,?,?,?,?,?,?)", [list(row)] + ) + print(f" Inserted {len(ORDERS_DATA)} rows") + + +def main() -> None: + parser = argparse.ArgumentParser(description="Create eval test tables in Teradata") + parser.add_argument("--drop-first", action="store_true", help="Drop tables before recreating") + args = parser.parse_args() + + db = os.environ.get("EVALS_DATABASE", "").strip() + if not db: + raise SystemExit("EVALS_DATABASE env var is not set — check your .env file") + + print(f"Connecting to Teradata (host={os.environ.get('TERADATA_HOST')}) ...") + with get_connection() as con: + with con.cursor() as cur: + if args.drop_first: + print("Dropping existing tables ...") + drop_tables(cur, db) + create_and_populate(cur, db) + + print("\nSetup complete. Run evals with: python run_evals.py") + + +if __name__ == "__main__": + main() diff --git a/tests/eval_test/suggest_overrides.py b/tests/eval_test/suggest_overrides.py new file mode 100644 index 0000000..50719bd --- /dev/null +++ b/tests/eval_test/suggest_overrides.py @@ -0,0 +1,163 @@ +""" +Draft description_overrides.json entries from failed eval cases. + +Usage: + uv run python suggest_overrides.py + uv run python suggest_overrides.py --type missing_parameter + uv run python suggest_overrides.py --summary results/latest_summary.json + uv run python suggest_overrides.py --dry-run + uv run python suggest_overrides.py --apply + uv run python suggest_overrides.py --apply --dry-run +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +from agent.client import DEFAULT_OVERRIDES_FILE +from judge.report import resolve_default_summary_path, resolve_suggestion_output_path +from judge.suggest_overrides import ( + DEFAULT_CASE_TYPES, + apply_suggestions_to_overrides, + generate_suggestions, +) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Suggest description_overrides.json entries from failed eval cases", + ) + parser.add_argument( + "--summary", + type=Path, + default=None, + help="Eval summary JSON to read (default: latest run from results/latest.json)", + ) + parser.add_argument( + "--output", + type=Path, + default=None, + help="Where to write the draft (default: alongside the source run, plus results/suggested_overrides.json)", + ) + parser.add_argument( + "--overrides-file", + type=Path, + default=DEFAULT_OVERRIDES_FILE, + help="Existing overrides file to include as context (default: description_overrides.json)", + ) + parser.add_argument( + "--type", + dest="case_type", + help=( + "Case type(s) to process — comma-separated subset of " + "ambiguous_selection, happy_path, missing_parameter, multi_tool " + f"(default: all)" + ), + ) + parser.add_argument("--dry-run", action="store_true", help="Generate: print prompts only. Apply: preview merge only.") + parser.add_argument( + "--apply", + action="store_true", + help="Merge reviewed suggestions into description_overrides.json (replaces existing tool entries)", + ) + parser.add_argument( + "--suggestions", + type=Path, + default=None, + help="Draft to apply when using --apply (default: latest run draft or results/suggested_overrides.json)", + ) + parser.add_argument( + "--tools", + help="Comma-separated tool names to apply when using --apply (default: all suggestions in the draft)", + ) + args = parser.parse_args() + + summary_path = args.summary or resolve_default_summary_path() + output_path = args.output or resolve_suggestion_output_path(summary_path) + suggestions_path = args.suggestions or output_path + + if args.apply: + only_tools = None + if args.tools: + only_tools = {part.strip() for part in args.tools.split(",") if part.strip()} + try: + result = apply_suggestions_to_overrides( + suggestions_path=suggestions_path, + overrides_path=args.overrides_file, + only_tools=only_tools, + dry_run=args.dry_run, + ) + except FileNotFoundError as exc: + print(exc, file=sys.stderr) + sys.exit(1) + except ValueError as exc: + print(f"ERROR: {exc}", file=sys.stderr) + sys.exit(1) + + if result.get("message"): + print(result["message"]) + sys.exit(0) + + applied = result.get("applied", []) + removed = result.get("removed", []) + target = result["overrides_path"] + if args.dry_run: + print(f"Dry run — would write {result['applied_count']} tool(s) → {target}") + else: + print(f"Applied {result['applied_count']} tool(s) → {target}") + if applied: + print(f" applied: {', '.join(applied)}") + if removed: + print(f" removed: {', '.join(removed)}") + if not args.dry_run: + print("Re-run evals with overrides:") + print(" uv run python run_evals.py --with-description-overrides") + sys.exit(0) + + try: + payload = generate_suggestions( + summary_path=summary_path, + output_path=output_path, + overrides_path=args.overrides_file, + case_type=args.case_type, + dry_run=args.dry_run, + ) + except FileNotFoundError as exc: + print(exc, file=sys.stderr) + sys.exit(1) + except ValueError as exc: + print(f"ERROR: {exc}", file=sys.stderr) + sys.exit(1) + except RuntimeError as exc: + print(f"ERROR: {exc}", file=sys.stderr) + sys.exit(1) + + if payload.get("message"): + print(payload["message"]) + if payload.get("skipped"): + for entry in payload["skipped"]: + print(f" skipped {entry['case_id']}: {entry['reason']}") + sys.exit(0) + + if args.dry_run: + print(json.dumps(payload, indent=2)) + sys.exit(0) + + suggestion_count = len(payload.get("suggestions", {})) + case_count = len(payload.get("cases", [])) + skipped_count = len(payload.get("skipped", [])) + print(f"Wrote {suggestion_count} tool suggestion(s) from {case_count} case(s) → {output_path}") + if output_path.name == "suggested_overrides.json" and output_path.parent.name != "results": + print(f"Also copied to results/suggested_overrides.json") + if skipped_count: + print(f"Skipped {skipped_count} case(s) — see 'skipped' in {output_path}") + print("Review the draft, then apply accepted entries:") + print(" uv run python suggest_overrides.py --apply") + print(" uv run python run_evals.py --with-description-overrides") + + +if __name__ == "__main__": + main() diff --git a/tests/eval_test/teardown_test_data.py b/tests/eval_test/teardown_test_data.py new file mode 100644 index 0000000..0d04081 --- /dev/null +++ b/tests/eval_test/teardown_test_data.py @@ -0,0 +1,43 @@ +""" +Drops the eval tables from EVALS_DATABASE. + +Usage: + python teardown_test_data.py +""" + +from __future__ import annotations + +import os + +import teradatasql +from dotenv import load_dotenv + +load_dotenv() + +TABLES = ["evals_employees", "evals_orders"] + + +def main() -> None: + db = os.environ.get("EVALS_DATABASE") + if not db: + raise SystemExit("EVALS_DATABASE env var is not set — check your .env file") + + print(f"Connecting to Teradata (host={os.environ.get('TERADATA_HOST')}) ...") + with teradatasql.connect( + host=os.environ["TERADATA_HOST"], + user=os.environ["TERADATA_USER"], + password=os.environ["TERADATA_PASSWORD"], + ) as con: + with con.cursor() as cur: + for table in TABLES: + try: + cur.execute(f"DROP TABLE {db}.{table}") + print(f" Dropped {db}.{table}") + except Exception as exc: + print(f" Skipped {db}.{table}: {exc}") + + print("Teardown complete.") + + +if __name__ == "__main__": + main() diff --git a/tests/eval_test/tests/__init__.py b/tests/eval_test/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/eval_test/tests/case_runner.py b/tests/eval_test/tests/case_runner.py new file mode 100644 index 0000000..feb6620 --- /dev/null +++ b/tests/eval_test/tests/case_runner.py @@ -0,0 +1,435 @@ +"""Run single-turn and shallow multi-turn eval cases.""" + +from __future__ import annotations + +import os +from typing import Any + +from deepeval.evaluate.configs import CacheConfig, DisplayConfig, ErrorConfig +from deepeval.evaluate.execute import execute_test_cases +from deepeval.test_case import LLMTestCase, ToolCall + +from agent.client import run_agent, run_agent_turns +from judge.checks import ToolCallRecord, run_deterministic_checks +from judge.metrics import clarification_metric, get_metrics, tool_correctness_metric +from judge.report import CaseEvalResult, build_recommendation, record_case_result + +MAX_TURNS = 7 + + +def _extract_exception_detail(exc: Exception) -> str: + """Extract detailed error message from exception, unwrapping ExceptionGroup if needed.""" + # For ExceptionGroup (TaskGroup errors), extract the first exception + if hasattr(exc, 'exceptions') and exc.exceptions: + # ExceptionGroup / TaskGroup + first_exc = exc.exceptions[0] + return _extract_exception_detail(first_exc) + # For regular exceptions, include traceback context if available + if hasattr(exc, '__cause__') and exc.__cause__: + return f"{type(exc).__name__}: {str(exc)} (caused by {type(exc.__cause__).__name__}: {str(exc.__cause__)})" + return f"{type(exc).__name__}: {str(exc)}" + + +def validate_multi_turn_case(case: dict) -> None: + """Validate a shallow multi-turn case schema.""" + turns = case.get("turns") + if turns is None: + return + + if not isinstance(turns, list): + raise ValueError(f"[{case.get('id')}] turns must be a list") + + if len(turns) < 2: + raise ValueError(f"[{case.get('id')}] multi-turn cases need at least 2 turns") + + if len(turns) > MAX_TURNS: + raise ValueError(f"[{case.get('id')}] multi-turn cases allow at most {MAX_TURNS} turns") + + for index, turn in enumerate(turns, start=1): + is_clarification = turn.get("expect") == "clarification" + has_tools = bool(turn.get("expected_tools")) + if is_clarification == has_tools: + raise ValueError( + f"[{case.get('id')}] turn {index} must set exactly one of " + "'expect': 'clarification' or non-empty 'expected_tools'", + ) + if "input" not in turn: + raise ValueError(f"[{case.get('id')}] turn {index} is missing 'input'") + + +def _to_tool_calls(records: list[ToolCallRecord]) -> list[ToolCall]: + return [ToolCall(name=tc.name, input_parameters=tc.input_parameters) for tc in records] + + +def _tool_dicts(records: list[ToolCallRecord]) -> list[dict[str, Any]]: + return [{"name": tc.name, "params": tc.input_parameters} for tc in records] + + +def _make_test_case( + *, + user_input: str, + response: str, + tools_called: list[ToolCallRecord], + expected_tools_raw: list[dict[str, Any]], +) -> LLMTestCase: + return LLMTestCase( + input=user_input, + actual_output=response, + tools_called=_to_tool_calls(tools_called), + expected_tools=[ + ToolCall(name=t["name"], input_parameters=t.get("params", {})) + for t in expected_tools_raw + ], + ) + + +def _evaluate_metrics(test_case: LLMTestCase, metrics) -> tuple[list[str], bool]: + test_result = execute_test_cases( + [test_case], + metrics, + error_config=ErrorConfig(ignore_errors=False, skip_on_missing_params=False), + display_config=DisplayConfig(verbose_mode=False, show_indicator=False), + cache_config=CacheConfig(write_cache=False, use_cache=False), + identifier="eval", + _use_bar_indicator=False, + _is_assert_test=True, + )[0] + + if test_result.success: + return [], True + + reasons: list[str] = [] + for metric_data in test_result.metrics_data or []: + if metric_data.error is not None or not metric_data.success: + detail = metric_data.reason or metric_data.error or "metric failed" + reasons.append(f"{metric_data.name}: {detail}") + return reasons, False + + +def _failure_result( + case: dict, + *, + case_input: str, + failure_stage: str, + failure_detail: str, + expected_tools: list[dict[str, Any]] | None = None, + actual_tools: list[dict[str, Any]] | None = None, + actual_output: str | None = None, + metric_reasons: list[str] | None = None, + turn_details: list[dict[str, Any]] | None = None, +) -> CaseEvalResult: + expected = expected_tools if expected_tools is not None else case.get("expected_tools", []) + metric_reasons = metric_reasons or [] + recommendation = build_recommendation( + case, + failure_stage=failure_stage, + failure_detail=failure_detail, + expected_tools=expected, + actual_tools=actual_tools, + metric_reasons=metric_reasons, + ) + return CaseEvalResult( + case_id=case.get("id", ""), + case_type=case.get("type", "happy_path"), + description=case.get("description", ""), + input=case_input, + expected_tools=expected, + passed=False, + failure_stage=failure_stage, + failure_detail=failure_detail, + actual_tools=actual_tools, + actual_output=actual_output, + metric_reasons=metric_reasons, + recommendation=recommendation, + turn_details=turn_details, + ) + + +def _success_result( + case: dict, + *, + case_input: str, + expected_tools: list[dict[str, Any]], + actual_tools: list[dict[str, Any]], + actual_output: str, + turn_details: list[dict[str, Any]] | None = None, +) -> CaseEvalResult: + return CaseEvalResult( + case_id=case.get("id", ""), + case_type=case.get("type", "happy_path"), + description=case.get("description", ""), + input=case_input, + expected_tools=expected_tools, + passed=True, + actual_tools=actual_tools, + actual_output=actual_output, + turn_details=turn_details, + ) + + +def run_single_turn_case(case: dict, bedrock_client, agent_model_id: str, judge_llm) -> CaseEvalResult: + """Run a single-turn case and return a structured result.""" + validate_multi_turn_case(case) + if "turns" in case: + raise ValueError(f"[{case.get('id')}] use run_eval_case() for multi-turn cases") + + try: + agent_result = run_agent( + prompt=case["input"], + model_id=agent_model_id, + bedrock_client=bedrock_client, + ) + except Exception as exc: + return _failure_result( + case, + case_input=case["input"], + failure_stage="agent", + failure_detail=_extract_exception_detail(exc), + ) + + raw_calls = [ + ToolCallRecord(name=tc.name, input_parameters=tc.input_parameters) + for tc in agent_result.tool_calls + ] + actual_tools = _tool_dicts(raw_calls) + det_errors = run_deterministic_checks(case, raw_calls) + if det_errors: + return _failure_result( + case, + case_input=case["input"], + failure_stage="deterministic", + failure_detail="; ".join(det_errors), + actual_tools=actual_tools, + actual_output=agent_result.final_response, + ) + + test_case = _make_test_case( + user_input=case["input"], + response=agent_result.final_response, + tools_called=raw_calls, + expected_tools_raw=case.get("expected_tools", []), + ) + metric_reasons, passed = _evaluate_metrics(test_case, get_metrics(case, judge_llm)) + if not passed: + return _failure_result( + case, + case_input=case["input"], + failure_stage="metric", + failure_detail="; ".join(metric_reasons), + actual_tools=actual_tools, + actual_output=agent_result.final_response, + metric_reasons=metric_reasons, + ) + + return _success_result( + case, + case_input=case["input"], + expected_tools=case.get("expected_tools", []), + actual_tools=actual_tools, + actual_output=agent_result.final_response, + ) + + +def run_multi_turn_case(case: dict, bedrock_client, agent_model_id: str, judge_llm) -> CaseEvalResult: + """Run and score a shallow multi-turn case (2–7 turns).""" + validate_multi_turn_case(case) + turns = case["turns"] + prompts = [turn["input"] for turn in turns] + case_input = " | ".join(f"Turn {index}: {turn['input']}" for index, turn in enumerate(turns, start=1)) + + max_steps_per_turn = int(os.environ.get("AGENT_MAX_STEPS_PER_TURN", "3")) + try: + turn_results = run_agent_turns( + prompts=prompts, + model_id=agent_model_id, + bedrock_client=bedrock_client, + max_steps_per_turn=max_steps_per_turn, + ) + except Exception as exc: + return _failure_result( + case, + case_input=case_input, + failure_stage="agent", + failure_detail=_extract_exception_detail(exc), + expected_tools=[], + ) + + conversation_prefix = "" + turn_details: list[dict[str, Any]] = [] + + for turn_number, (turn_spec, turn_result) in enumerate(zip(turns, turn_results, strict=True), start=1): + raw_calls = [ + ToolCallRecord(name=tc.name, input_parameters=tc.input_parameters) + for tc in turn_result.tool_calls + ] + actual_tools = _tool_dicts(raw_calls) + turn_label = f"{case.get('id')} turn {turn_number}" + turn_input = f"{conversation_prefix}User: {turn_spec['input']}" + + if turn_spec.get("expect") == "clarification": + pseudo_case = {"id": turn_label, "type": "missing_parameter", "expected_tools": []} + det_errors = run_deterministic_checks(pseudo_case, raw_calls) + if det_errors: + turn_details.append( + { + "turn": turn_number, + "input": turn_spec["input"], + "mode": "clarification", + "passed": False, + "failure_stage": "deterministic", + "failure_detail": "; ".join(det_errors), + "actual_tools": actual_tools, + } + ) + return _failure_result( + case, + case_input=case_input, + failure_stage="deterministic", + failure_detail=f"turn {turn_number}: {'; '.join(det_errors)}", + expected_tools=[], + actual_tools=actual_tools, + actual_output=turn_result.final_response, + turn_details=turn_details, + ) + + test_case = _make_test_case( + user_input=turn_input, + response=turn_result.final_response, + tools_called=[], + expected_tools_raw=[], + ) + metric_reasons, passed = _evaluate_metrics(test_case, [clarification_metric(judge_llm)]) + else: + expected_tools = turn_spec.get("expected_tools", []) + pseudo_case = { + "id": turn_label, + "type": "happy_path", + "expected_tools": expected_tools, + } + det_errors = run_deterministic_checks(pseudo_case, raw_calls) + if det_errors: + turn_details.append( + { + "turn": turn_number, + "input": turn_spec["input"], + "mode": "tool", + "passed": False, + "failure_stage": "deterministic", + "failure_detail": "; ".join(det_errors), + "expected_tools": expected_tools, + "actual_tools": actual_tools, + } + ) + return _failure_result( + case, + case_input=case_input, + failure_stage="deterministic", + failure_detail=f"turn {turn_number}: {'; '.join(det_errors)}", + expected_tools=expected_tools, + actual_tools=actual_tools, + actual_output=turn_result.final_response, + turn_details=turn_details, + ) + + test_case = _make_test_case( + user_input=turn_input, + response=turn_result.final_response, + tools_called=raw_calls, + expected_tools_raw=expected_tools, + ) + metric_reasons, passed = _evaluate_metrics(test_case, [tool_correctness_metric(judge_llm)]) + + if not passed: + turn_details.append( + { + "turn": turn_number, + "input": turn_spec["input"], + "mode": "clarification" if turn_spec.get("expect") == "clarification" else "tool", + "passed": False, + "failure_stage": "metric", + "failure_detail": "; ".join(metric_reasons), + "expected_tools": turn_spec.get("expected_tools", []), + "actual_tools": actual_tools, + } + ) + return _failure_result( + case, + case_input=case_input, + failure_stage="metric", + failure_detail=f"turn {turn_number}: {'; '.join(metric_reasons)}", + expected_tools=turn_spec.get("expected_tools", []), + actual_tools=actual_tools, + actual_output=turn_result.final_response, + metric_reasons=metric_reasons, + turn_details=turn_details, + ) + + turn_details.append( + { + "turn": turn_number, + "input": turn_spec["input"], + "mode": "clarification" if turn_spec.get("expect") == "clarification" else "tool", + "passed": True, + "actual_tools": actual_tools, + } + ) + conversation_prefix += f"User: {turn_spec['input']}\nAssistant: {turn_result.final_response}\n" + + last_turn = turn_results[-1] + last_tools = [ + ToolCallRecord(name=tc.name, input_parameters=tc.input_parameters) + for tc in last_turn.tool_calls + ] + return _success_result( + case, + case_input=case_input, + expected_tools=turns[-1].get("expected_tools", []), + actual_tools=_tool_dicts(last_tools), + actual_output=last_turn.final_response, + turn_details=turn_details, + ) + + +def run_eval_case(case: dict, bedrock_client, agent_model_id: str, judge_llm) -> CaseEvalResult: + """Run any eval case and return a structured result.""" + validate_multi_turn_case(case) + if "turns" in case: + return run_multi_turn_case(case, bedrock_client, agent_model_id, judge_llm) + return run_single_turn_case(case, bedrock_client, agent_model_id, judge_llm) + + +def build_test_case(case: dict, bedrock_client, agent_model_id: str) -> LLMTestCase: + """Run a single-turn case and return a deepeval LLMTestCase (metrics not scored).""" + validate_multi_turn_case(case) + if "turns" in case: + raise ValueError(f"[{case.get('id')}] use assert_eval_case() for multi-turn cases") + + agent_result = run_agent( + prompt=case["input"], + model_id=agent_model_id, + bedrock_client=bedrock_client, + ) + raw_calls = [ + ToolCallRecord(name=tc.name, input_parameters=tc.input_parameters) + for tc in agent_result.tool_calls + ] + det_errors = run_deterministic_checks(case, raw_calls) + if det_errors: + case_id = case.get("id", "") + raise AssertionError(f"[{case_id}] deterministic check failed: {'; '.join(det_errors)}") + + return _make_test_case( + user_input=case["input"], + response=agent_result.final_response, + tools_called=raw_calls, + expected_tools_raw=case.get("expected_tools", []), + ) + + +def assert_eval_case(case: dict, bedrock_client, agent_model_id: str, judge_llm) -> None: + """Run and score any eval case (single- or multi-turn).""" + result = run_eval_case(case, bedrock_client, agent_model_id, judge_llm) + record_case_result(result) + if not result.passed: + detail = result.failure_detail or "; ".join(result.metric_reasons) or "eval case failed" + raise AssertionError(f"[{result.case_id}] {result.failure_stage} check failed: {detail}") diff --git a/tests/eval_test/tests/conftest.py b/tests/eval_test/tests/conftest.py new file mode 100644 index 0000000..ae44f8b --- /dev/null +++ b/tests/eval_test/tests/conftest.py @@ -0,0 +1,107 @@ +"""Shared fixtures and helpers for the Teradata MCP eval suite.""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +import pytest +from dotenv import load_dotenv + +load_dotenv() + +CASES_DIR = Path(__file__).parent.parent / "cases" +MODULES = ["base", "dba", "sec", "qlty", "chat", "plot", "tmpl"] + + +def _substitute(obj, evals_db: str): + """Recursively replace {EVALS_DATABASE} in strings within dicts/lists.""" + if isinstance(obj, str): + return obj.replace("{EVALS_DATABASE}", evals_db) + if isinstance(obj, dict): + return {k: _substitute(v, evals_db) for k, v in obj.items()} + if isinstance(obj, list): + return [_substitute(i, evals_db) for i in obj] + return obj + + +def load_cases(module: str) -> list[dict]: + path = CASES_DIR / f"{module}.json" + if not path.exists(): + return [] + data = json.loads(path.read_text()) + # Filter out comment/instruction stubs + return [c for c in data.get("cases", []) if "id" in c] + + +def build_test_case(case: dict, bedrock_client, agent_model_id: str): + """Run a single-turn case and return a deepeval LLMTestCase.""" + from tests.case_runner import build_test_case as _build_test_case + + evals_db = os.environ.get("EVALS_DATABASE", "").strip() + resolved = _substitute(case, evals_db) + return _build_test_case(resolved, bedrock_client, agent_model_id) + + +def assert_eval_case(case: dict, bedrock_client, agent_model_id: str, judge_llm) -> None: + """Run and score any eval case (single- or multi-turn).""" + from tests.case_runner import assert_eval_case as _assert_eval_case + + evals_db = os.environ.get("EVALS_DATABASE", "").strip() + resolved = _substitute(case, evals_db) + _assert_eval_case(resolved, bedrock_client, agent_model_id, judge_llm) + + +@pytest.fixture(scope="session") +def bedrock_client(): + import boto3 + region = os.environ.get("AWS_REGION", "us-east-1") + return boto3.client("bedrock-runtime", region_name=region) + + +@pytest.fixture(scope="session") +def agent_model_id() -> str: + return os.environ.get("BEDROCK_MODEL_ID", "anthropic.claude-3-5-sonnet-20241022-v2:0") + + +@pytest.fixture(scope="session") +def judge_llm(bedrock_client): + from judge.bedrock_llm import BedrockLLM + return BedrockLLM(bedrock_client=bedrock_client) + + +def pytest_sessionstart(session) -> None: + """Initialize eval result collection for live eval runs.""" + from agent.client import get_description_override_status + from judge.report import begin_eval_run + + agent_model_id = os.environ.get("BEDROCK_MODEL_ID", "anthropic.claude-3-5-sonnet-20241022-v2:0") + judge_model_id = os.environ.get("BEDROCK_JUDGE_MODEL_ID", agent_model_id) + override_status = get_description_override_status() + begin_eval_run( + agent_model_id=agent_model_id, + judge_model_id=judge_model_id, + evals_database=os.environ.get("EVALS_DATABASE", "").strip(), + description_mode=str(override_status["mode"]), + description_overrides_file=override_status.get("file"), # type: ignore[arg-type] + description_override_count=int(override_status.get("tool_count") or 0), + ) + + +def pytest_sessionfinish(session, exitstatus) -> None: + """Write a markdown/json summary when live eval cases were executed.""" + from judge.report import get_current_report, write_eval_summary + + report = get_current_report() + if report is None or not report.results: + return + + artifacts = write_eval_summary(report) + terminal = session.config.pluginmanager.get_plugin("terminalreporter") + if terminal is not None: + terminal.write_line("") + terminal.write_line(f"Eval run: {artifacts.run_id}") + terminal.write_line(f"Run directory: results/{artifacts.run_dir.name}") + terminal.write_line("Summary: results/latest_summary.md (copy of this run)") + terminal.write_line("Index: results/index.json") diff --git a/tests/eval_test/uv.lock b/tests/eval_test/uv.lock new file mode 100644 index 0000000..d6cdb10 --- /dev/null +++ b/tests/eval_test/uv.lock @@ -0,0 +1,2101 @@ +version = 1 +revision = 1 +requires-python = ">=3.11" + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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