SAS Viya MCP Server

A Model Context Protocol (MCP) server for executing SAS code, training AutoML projects, scoring models and so much more for SAS Viya environments.

Features

68 tools across 8 selectable tiers, spanning the Analytics Life Cycle on SAS Viya
Prompt Templates for improving your SAS Code
OAuth2 authentication with PKCE flow
HTTP-based MCP server compatible with MCP clients

Articles & Videos

Here you can find getting articles on how to use and integrate the SAS MCP Server in different tools and what to build with it:

Getting Started

Prerequisites

Required
- Python 3.12+
- uv 0.8+
- SAS Viya environment with compute service
- Setup the Viya environment for MCP
  - See configuration.md
Optional
- Docker: refer to docker setup

Installation

Clone the repository:

git clone <repository-url>
cd sas-mcp-server

Install dependencies

uv sync

NOTE: This will by default create a virtual environment called .venv in the project's root directory.

If for some reason the virtual environment is not created, please run uv venv and then re-run uv sync.

Usage

Configure environment variables:

cp .env.sample .env

Edit .env and set

VIYA_ENDPOINT=https://your-viya-server.com

Start the MCP server (see Choosing a deployment mode below):

Option A: HTTP mode (pre-run the server, connect from MCP client)

uv run app

The server will be available at http://localhost:8134/mcp by default. Authentication is handled via OAuth2 PKCE flow in the browser.

Option B: Stdio mode (MCP client starts the server on demand)

Authenticate once. Two equivalent options:

# Option B1 — if you have the SAS Viya CLI installed:
sas-viya auth loginCode

# Option B2 — built-in helper, no external CLI needed (Viya 2022.11+):
uv run sas-mcp-login

Both flows write an access token to a local cache (~/.sas/credentials.json and ~/.sas-mcp-server/credentials.json respectively); the stdio server reads whichever it finds. When the token expires, re-run the same command.

Then configure your MCP client to launch the server directly (see below).

Option C: Docker / Podman (containerized deployment)

Pull the pre-built image from GitHub Container Registry:

docker pull ghcr.io/sassoftware/sas-mcp-server:latest
docker run -e VIYA_ENDPOINT=https://your-viya-server.com -p 8134:8134 ghcr.io/sassoftware/sas-mcp-server:latest

Or build locally from source:

docker build -t sas-mcp-server .
docker run -e VIYA_ENDPOINT=https://your-viya-server.com -p 8134:8134 sas-mcp-server

Available image tags:

latest — most recent tagged release
<major>.<minor>.<patch> (e.g. 1.0.0) — specific release
<major>.<minor> (e.g. 1.0) — latest patch of a minor release
edge — tip of main (unreleased, for testing)
sha-<short> — pinned to a specific commit

Programmatic clients with a pre-existing Viya token

If your caller already holds a Viya access token (e.g. an automation script that obtained one via the SAS Viya CLI), start the HTTP-mode server with ALLOW_RAW_BEARER=true and pass the token directly:

curl -H "Authorization: Bearer $VIYA_TOKEN" http://localhost:8134/mcp ...

The server validates the token against Viya's JWKS and uses it upstream as-is, bypassing the MCP JWT swap. The default OAuth2 PKCE flow keeps working alongside — both client types share the same /mcp endpoint.

Choosing a deployment mode

	HTTP	Stdio	Docker
How it runs	Long-running server you start separately	MCP client spawns it on demand	Containerized HTTP server
Authentication	OAuth2 PKCE flow (browser popup)	Cached token via `sas-viya` CLI or `sas-mcp-login`	OAuth2 PKCE flow (browser popup)
Best for	Multi-user or shared setups; production-like environments	Single-user local development; quick experimentation	Team deployments; CI/CD; environments without Python installed
Requires	Python + uv	Python + uv (+ optional `sas-viya` CLI)	Docker or Podman only
Credentials stored?	No — user authenticates interactively	No — only an access token (not a password) is cached	No — user authenticates interactively
MCP client config	Point client to `http://localhost:8134/mcp`	Client runs `uv run app-stdio`	Point client to `http://host:8134/mcp`

Quick guidance:

Starting out or exploring? Use stdio — one sas-viya auth loginCode or uv run sas-mcp-login, then your MCP client manages the server lifecycle.
Need secure, interactive auth? Use HTTP — no stored passwords, each user authenticates via browser.
Deploying for a team or on a server? Use Docker — portable, no Python dependency on the host, easy to integrate with orchestrators.
Using Gemini CLI? Use stdio — Gemini CLI does not support HTTP mode or browser-based OAuth. See Gemini CLI configuration.

Limiting exposed tools (tiers)

Tools are grouped into numbered tiers. By default the server exposes all of them; set MCP_TIERS to expose only a subset — handy for keeping a client's tool list small and focused, or hiding capabilities a deployment shouldn't offer. Accepts ranges and comma lists (e.g. MCP_TIERS=0-4 or MCP_TIERS=0,1,6,7); unset means all tiers.

Tier	Group
0	Compute Contexts & Code Execution
1	Data Discovery
2	Data Operations & Files
3	Reports & Visualization
4	Batch Jobs & Async Execution
5	Automated Machine Learning
6	Model Management & Scoring
7	Decisioning (SAS Intelligent Decisioning)

# Example: expose only compute/discovery/data-ops and reporting
MCP_TIERS=0-3 uv run app

Available Tools

The headings below match the numbered tiers above, so MCP_TIERS maps directly to the tools you expose (e.g. MCP_TIERS=0-3 gives Tiers 0–3).

Tier 0 — Compute Contexts & Code Execution

execute_sas_code: Execute SAS code snippets and retrieve execution results (log and listing output). Runs in a reusable, per-user compute session that is kept warm across calls, so SAS state (WORK tables, macro variables, assigned librefs) persists between successive calls — use reset_compute_session to start fresh.
list_compute_contexts: List available compute contexts
reset_compute_session: Delete the cached compute session for a context, discarding its SAS state and forcing a fresh session on the next call

Tier 1 — Data Discovery

Information Catalog (metadata discovery & profiling):

catalog_search: Search the catalog for assets (tables, columns, reports, …) using the SAS catalog search grammar (free text, facets like AssetType:Report, ranges). Each hit carries a resource_uri you can hand to the matching tool (e.g. get_report, get_castable_data).
catalog_search_helper: Discover how to query the catalog — list the available facets, or the valid values for one facet — so you can build precise catalog_search queries.
catalog_find_instance: Resolve the catalog instance for a source-asset resource_uri, bridging a search hit to the profiling and download tools without handling an instance id by hand.
catalog_run_adhoc_analysis: Submit an ad-hoc profiling job for a table. NLP enrichment (language, sentiment, semantic IDs) is on by default, populating informationPrivacy, nlpTerms, nlpTags, and mostImportantFields.
catalog_get_adhoc_analysis: Poll a profiling job and cross-check the target instance, reporting profile_ready once results have landed on the asset — so a download isn't fired too early.
catalog_download_table_profile: Download a table's data dictionary and column profile as CSV, identified by either instance_id or resource_uri.
catalog_list_agents: List the catalog's discovery agents (the crawlers that populate metadata).
catalog_run_agent: Start a discovery agent run (asynchronous) to crawl its data source and refresh catalog metadata.
catalog_get_agent_history: Inspect an agent's run history — status and how much metadata each run enumerated/added/updated/removed.

CAS data (in-memory):

list_cas_servers: List available CAS servers
list_caslibs: List CAS libraries on a server
list_castables: List tables in a CAS library
list_source_tables: List source tables not yet loaded into memory (candidates for promotion)
get_castable_info: Get table metadata (row count, columns, size)
get_castable_columns: Get column names, types, labels, formats
get_castable_data: Fetch sample rows from a CAS table

Compute libraries (SAS/Compute, within a compute context):

list_compute_libraries: List the SAS libraries (librefs) assigned in a compute context
list_compute_tables: List the tables in a SAS library within a compute context
list_compute_columns: List the columns of a table in a SAS library

Tier 2 — Data Operations & Files

upload_data: Upload a data file into a CAS table — read server-side so the data never passes through the model's context — from file_path (the server reads it off disk) or url (the server fetches it and converts it to the multipart upload the endpoint requires). Ingests the formats the casManagement uploadTable API accepts — csv, tsv (csv + tab delimiter), xls, xlsx (single sheet), sas7bdat, sashdat — auto-detected from the extension or set with data_format. parquet is not accepted by that endpoint and is rejected up front with guidance (load via a path-based caslib + promote_table_to_memory, or convert to csv/sas7bdat).
upload_inline_data: Create a small CAS table from inline csv/tsv text passed as a string (a lookup/mapping table the model builds on the fly, or a quick test table). The payload travels through the model's context, so it's for tiny tables only — use upload_data for files or anything larger.
promote_table_to_memory: Load a source table into memory at global scope (idempotent)
list_files: List files in the Viya Files Service
upload_file: Upload a file to Viya Files Service
download_file: Download file content

Tier 3 — Reports & Visualization

list_reports: List Visual Analytics reports
get_report: Get report metadata and definition
export_report: export a report (or specific report objects) in any format the VA service supports — package (zip), pdf, png, svg, csv, tsv, xlsx, or summary. Text formats come back inline, png as image content, and binary formats (package/pdf/xlsx) as an embedded file with the right MIME type.

Tier 4 — Batch Jobs & Async Execution

submit_batch_job: Submit a SAS job for async execution
get_job_status: Check job state
list_jobs: List recent/running jobs
cancel_job: Cancel a running job
get_job_log: Retrieve job log

Tier 5 — Automated Machine Learning

list_ml_projects: List AutoML projects
create_ml_project: Create a new AutoML project from a loaded, global-scope CAS table (caslib + table + optional CAS server)
run_ml_project: Run pipeline automation
register_ml_champion_model: Register an AutoML project's champion model to the Model Repository
publish_ml_champion_model: Publish an AutoML project's champion model to a scoring destination

Tier 6 — Model Management & Scoring

list_registered_models: List models in repository
list_publishing_destinations: List available scoring/publishing destinations, for use with publish_ml_champion_model
list_mas_modules: List published MAS modules
get_mas_module_step_signature: Inspect a published MAS module step's input/output variable signature before scoring
score_data: Score data against a published model or decision

Tier 7 — Decisioning (SAS Intelligent Decisioning)

Build and manage SAS Intelligent Decisioning rule sets and decision flows end to end, then publish a flow to Micro Analytic Score (MAS) so score_data can execute it.

Business rules — rule sets:

create_business_ruleset / update_business_ruleset / get_business_ruleset / list_business_rulesets / delete_business_ruleset: Manage rule sets (the input/output signature the rules operate on)
lock_business_ruleset_revision: Lock the current rule set state as an immutable revision (what a decision step references)
list_business_ruleset_revisions: List a rule set's locked revisions

Business rules — rules:

create_business_rule / update_business_rule / get_business_rule / list_business_rules / delete_business_rule: Manage the conditional rules inside a rule set

Decision flows:

create_decision_flow / update_decision_flow / get_decision_flow / list_decision_flows / delete_decision_flow: Manage decision flows that chain rule set steps
get_decision_flow_code: Retrieve the generated DS2 execution code for a flow
lock_decision_flow_revision / list_decision_flow_revisions / get_decision_flow_revision: Lock, list, and fetch immutable decision revisions
publish_decision_flow: Publish a locked decision revision to a MAS destination, polling to completion and returning the server-generated MAS moduleId (directly usable with get_mas_module_step_signature / score_data)

Prompt Templates

debug_sas_log: Analyze SAS log for errors with root-cause explanations
explore_dataset: Generate data-profiling SAS code
data_quality_check: Generate DQ assessment code
statistical_analysis: Set up a statistical workflow with diagnostics
optimize_sas_code: Review and optimize SAS code
explain_sas_code: Block-by-block code explanation
sas_macro_builder: Build production-quality SAS macros
generate_report: Generate ODS/PROC REPORT code

MCP Client Configuration

Example configurations are provided in the examples/ folder. Below are quick-start snippets for common clients.

VS Code / Cursor / Claude Code (`.vscode/mcp.json`)

HTTP mode (requires uv run app running separately):

{
    "servers": {
        "sas-execution-mcp": {
            "url": "http://localhost:8134/mcp",
            "type": "http"
        }
    }
}

Stdio mode (starts the server on demand):

{
    "servers": {
        "sas-execution-mcp": {
            "command": "uv",
            "args": ["run", "app-stdio"],
            "cwd": "${workspaceFolder}"
        }
    }
}

Gemini CLI (`.gemini/settings.json`)

Gemini CLI only supports stdio mode. Add to your ~/.gemini/settings.json or project-level .gemini/settings.json:

{
    "mcpServers": {
        "sas-viya-mcp": {
            "command": "uv",
            "args": ["run", "app-stdio"],
            "cwd": "/path/to/sas-mcp-server",
            "timeout": 60000
        }
    }
}

Note: The timeout field (in milliseconds) is important — SAS Viya API calls can take longer than the Gemini CLI default of 10 seconds. A value of 60000 (60s) is recommended. Set cwd to the absolute path of your sas-mcp-server checkout.

Example

Execute SAS code through the MCP tool:

data work.students;
input Name $ Age Grade $;
datalines;
Alice 20 A
Bob 22 B
;
run;

proc print data=work.students;
run;

For more details, configuration options, and deployment options, please refer to the examples folder and follow the instructions listed there.

Collection Mode (Usage Telemetry)

An opt-in, off-by-default mode that records how the server is actually used — which tools, for what goals, with what inputs, and where they fall short. It serves two audiences:

Contributors giving structured feedback to the maintainers. Rather than filing prose bug reports, you can turn it on for a while and share the resulting log so maintainers can see which tools are used, which fail, and what goals have no good tool yet — a direct signal for improving existing tools and identifying new ones.
Organizations running the server for their own users. Teams that deploy the MCP server internally can enable it to understand what their users do with it and why, entirely within their own infrastructure.

It is implemented as a FastMCP middleware wrapper (telemetry.py + usage_logger.py) and requires no changes to any tool.

🔒 Nothing is ever sent anywhere automatically. Collection mode only appends to a local log file on the machine running the server. It is disabled unless you explicitly enable it, and even when enabled the data stays on your disk — sharing it with anyone (including the maintainers) is a deliberate, manual step you take by sending the file yourself. There is no phone-home, no network transmission, and no third party involved.

When enabled it does two things:

Injects a required goal parameter into every tool's schema, asking the model to state in one sentence why it chose that tool for the current request. The goal is stripped from the arguments before the real tool runs, so tools never see it.
Appends one JSON line per tool call (JSON Lines / NDJSON) to a local log file: timestamp, session id, tool name, goal, arguments, result, status, error, and latency. Secret-shaped keys and inline Bearer/JWT tokens are redacted and every field is size-capped.

Enabling it

Set the toggle in .env (all options are documented in .env.sample):

COLLECTION_MODE=true
# optional overrides (defaults shown):
# COLLECTION_LOG_PATH=~/.sas-mcp-server/tool-usage.log
# COLLECTION_LOG_RESULTS=false   # false = record result shape only, not contents

By default (COLLECTION_LOG_RESULTS=false) tool results are recorded only as a content-free shape summary (e.g. {"_type":"array","_items":500}) — arguments, goal, status, and error text are still captured. Set it to true to capture (capped + redacted) result contents for richer analysis.

⚠️ Privacy: when enabled, the log captures your tool inputs (e.g. the SAS code and queries you submit) and — if COLLECTION_LOG_RESULTS=true — real result data that may include table rows, SAS listings, and PII. Redaction is heuristic (credential-shaped keys + Bearer/JWT only) and does not detect PII in data values. Review the log before sharing it. The file is locked to your user (chmod 0600 on POSIX; icacls on Windows, best-effort).

Performance impact

Collection mode is designed to be cheap enough to leave on. Measured on this repo (45 registered tools, FastMCP 3.4.2):

Prompt tokens. The injected goal field grows the tools/list schema the model sees by roughly +2,400 input tokens (~29%) per turn. Because the tool list is stable within a session it is served from the prompt cache after the first turn (steady-state ≈ +240 tokens/turn), plus ~15–30 output tokens per call for the model to write the goal sentence. This is the only client-visible cost and it applies only while collection mode is enabled.
Per-call latency. Middleware + logging adds ≈1.4 ms per call at the shape-only default (≈5.3 ms with COLLECTION_LOG_RESULTS=true). The JSONL write is offloaded to a worker thread so it never blocks the event loop. Against real Viya calls (typically hundreds of milliseconds to seconds) this is negligible — the live integration suite passed identically with collection mode off and on, the overhead lost in normal network variance.
Disk. Roughly 0.5–0.7 KB per tool call at the shape-only default. The log rotates at COLLECTION_MAX_LOG_BYTES (default 10 MiB, ≈16k calls) and keeps COLLECTION_LOG_BACKUPS (default 3) rotated files, so on-disk growth is bounded.

Testing

The project includes two layers of tests: unit tests (fast, no credentials required) and integration tests (run against a real SAS Viya instance).

run_tests.sh vs. running pytest directly — pick by platform. run_tests.sh is a Bash convenience wrapper (it adds the ruff + pyright gates, credential wiring, and JUnit reporting). It runs on Linux/macOS — and on Windows only under Git Bash or WSL. On Windows PowerShell or cmd, use the uv run python -m pytest … commands shown under each mode below. They are cross-platform, do the same test selection, and need no setup beyond uv sync.

Running Unit Tests

Unit tests verify tool schemas, request payloads, and internal logic without making any network calls:

./run_tests.sh                                     # Linux/macOS (also runs ruff + pyright)
uv run python -m pytest -m "not integration" -v    # any platform, incl. Windows PowerShell

This runs the unit suite and deselects the integration tests, which then show up in the summary as e.g. 28 deselected. That is expected — those tests are not meant to run in a unit-only pass. They only execute in the integration modes below, because they need a live Viya instance; there is no flag that "activates" them in a not integration run.

Running Integration Tests

Integration tests call every tool against a live Viya environment. They require credentials, provided via .env or CLI arguments.

uv sync installs everything the integration suite needs, including openpyxl (used to build the Excel upload_data fixture). It lives in the test-formats dependency group, which [tool.uv] default-groups syncs by default — so no extra install step is required.

Full suite (unit + integration) — reads VIYA_ENDPOINT, VIYA_USERNAME, VIYA_PASSWORD from .env:

./run_tests.sh --integration      # Linux/macOS
uv run python -m pytest -v        # any platform

Passing credentials on the command line (wrapper only):

./run_tests.sh --integration \
    --endpoint https://your-viya-server.com \
    --username youruser \
    --password yourpassword

With the direct pytest command, set the same three variables in .env (or export them in your shell) instead.

Integration tests only (skip unit tests):

./run_tests.sh --integration-only                    # Linux/macOS
uv run python -m pytest -m integration --no-cov -v   # any platform

The pytest marker is integration, not integration-only. --integration-only is a flag of the run_tests.sh wrapper; the underlying pytest marker is just integration. Running pytest -m "integration-only" matches no marker and silently deselects all tests (0 selected). Use -m integration.

Why --no-cov? pytest.ini enforces a 90% coverage floor that only the full unit suite reaches. An integration-only run exercises far less code (~65%), so without --no-cov pytest exits non-zero with a coverage failure even though every selected test passed. run_tests.sh --integration-only adds --no-cov for you; add it yourself when calling pytest directly (or use --cov-fail-under=0).

Binary upload formats. The Excel upload_data integration test generates its .xlsx fixture with openpyxl, from the test-formats group that uv sync installs by default (see above). If you deliberately sync without it (e.g. uv sync --no-default-groups), the test importorskips — you'll see it as skipped, not failed. csv, tsv, and file_path/data_format coverage needs no extra deps. Generating a sas7bdat/sashdat fixture requires SAS itself, so those two formats are covered by unit-level payload tests only, not live.

Every one of the 68 tools and 8 prompt templates has an integration test, enforced by the test_every_tool_has_integration_coverage / test_every_prompt_has_integration_coverage guards — adding a new tool or prompt without integration coverage fails the suite. The resource-dependent tests discover real targets on the instance: score_data scores the most recently modified MAS module (discovering a real step and its inputs), and run_ml_project re-runs the most recently modified completed ML project. They skip only if the instance has no such resource at all. Likewise, test_catalog_agents_workflow skips with "No discovery agent named 'Public'" on instances where SAS Information Catalog has no discovery agent named Public configured — an expected skip, not a failure; ask a Viya admin to configure one if you need that test to run.

In CI: the .github/workflows/integration.yml workflow runs this suite on demand (manual dispatch, or by adding the run-integration label to a PR) using repository secrets, and publishes the results back to the PR as a status check, a sticky comment, and a downloadable JUnit artifact. Result files are written to reports/ (git-ignored) and are never committed.

Locally (attach results to a PR yourself): run with --report to write the JUnit XML and a Markdown summary into reports/ (git-ignored), then post them to a PR with the GitHub CLI — no commit, no CI required:

./run_tests.sh --integration-only --report
gh pr comment <PR> --body-file reports/integration-summary.md   # summary table as a comment
gh gist create reports/integration.xml                          # full XML as a linkable gist

GitHub has no API/CLI to attach a binary file to a PR (drag-and-drop upload is browser-only), so the summary is posted as a comment and the raw XML is shared via a gist link or pasted in a collapsed <details> block. To produce the canonical Actions artifact from your machine instead, trigger the workflow remotely: gh workflow run integration.yml.

Test Structure

File	Description
`tests/test_tool_payloads.py`	Payload assertions for all 68 tools (URL paths, JSON body, query params, headers) plus error-path coverage
`tests/test_integration.py`	End-to-end workflow tests against a real Viya instance
`tests/test_tools.py`	Unit tests for the generic Viya REST helpers in `viya_client` (`get_json`, `post_json`, `make_client`, …)
`tests/test_viya_utils.py`	Unit tests for Viya compute session and job orchestration
`tests/test_mcp_server.py`	Unit tests for the HTTP auth middleware, health route, and token getter
`tests/test_config.py`	Unit tests for configuration loading
`tests/test_config_oauth.py`	Unit tests for `PermissiveOAuthProxy` raw-bearer handling
`tests/test_auth_login.py`	Unit tests for the `sas-mcp-login` OAuth/PKCE helper
`tests/test_stdio_server.py`	Unit tests for stdio token resolution and the device-code flow
`tests/test_env.py`	Unit tests for the `env_bool` helper
`tests/test_prompts.py`	Unit tests for prompt template rendering

Contributing

Maintainers are accepting patches and contributions to this project. Please read CONTRIBUTING.md for details about submitting contributions to this project.

License & Attribution

Except for the the contents of the /static folder, this project is licensed under the Apache 2.0 License. Elements in the /static folder are owned by SAS and are not released under an open source license. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

Separate commercial licenses for SAS software (e.g., SAS Viya) are not included and are required to use these capabilities with SAS software.

As with any container image, direct and indirect dependencies are governed by their own licenses. Users of the published container image are responsible for ensuring that their use complies with all applicable licenses.

All third-party trademarks referenced belong to their respective owners and are only used here for identification and reference purposes, and not to imply any affiliation or endorsement by the trademark owners.

Third-Party Dependencies

This project requires the following dependencies.

Dependency	License
Python	Python Software License
FastMCP	Apache License 2.0
uvicorn	BSD 3-Clause License
starlette	BSD 3-Clause License
httpx	MIT License

Name		Name	Last commit message	Last commit date
Latest commit History 69 Commits
.github/workflows		.github/workflows
examples		examples
scripts		scripts
src/sas_mcp_server		src/sas_mcp_server
static		static
tests		tests
.dockerignore		.dockerignore
.env.sample		.env.sample
.gitignore		.gitignore
.python-version		.python-version
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
ContributorAgreement.txt		ContributorAgreement.txt
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
SUPPORT.md		SUPPORT.md
TROUBLESHOOTING.md		TROUBLESHOOTING.md
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
run_tests.sh		run_tests.sh
uv.lock		uv.lock

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SAS Viya MCP Server

Features

Articles & Videos

Getting Started

Prerequisites

Installation

Usage

Choosing a deployment mode

Limiting exposed tools (tiers)

Available Tools

Tier 0 — Compute Contexts & Code Execution

Tier 1 — Data Discovery

Tier 2 — Data Operations & Files

Tier 3 — Reports & Visualization

Tier 4 — Batch Jobs & Async Execution

Tier 5 — Automated Machine Learning

Tier 6 — Model Management & Scoring

Tier 7 — Decisioning (SAS Intelligent Decisioning)

Prompt Templates

MCP Client Configuration

VS Code / Cursor / Claude Code (.vscode/mcp.json)

Gemini CLI (.gemini/settings.json)

Example

Collection Mode (Usage Telemetry)

Enabling it

Performance impact

Testing

Running Unit Tests

Running Integration Tests

Test Structure

Contributing

License & Attribution

Third-Party Dependencies

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VS Code / Cursor / Claude Code (`.vscode/mcp.json`)

Gemini CLI (`.gemini/settings.json`)

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