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feat: Phase 1.5 — benchmark results for 12 MCP servers + blog post
Evaluated 12 MCP servers, 5 completed successfully: - mcp-sequential-thinking: 77/100 (100% reliability) - mcp-datetime: 75/100 (73% reliability, 10 tools) - context7: 73/100 (100% reliability) - mcp-everything: 68/100 (77% reliability, 13 tools) - mcp-filesystem: 62/100 (14% reliability — path generation issue) 7 servers failed due to MCP SDK connection timeout during Claude API calls. This is a known issue — the MCP server disconnects when idle during the ~30s task generation phase. Will be fixed by parallelizing or pre-generating. Includes: - results/ — raw evaluation data (report.json per server) - docs/blog/mcp-server-benchmark.md — generated blog post with rankings - README.md — project overview with quick start Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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‎README.md‎

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# agent-eval
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**The credit rating agency for AI agents.** Open-source evaluation framework for benchmarking MCP servers, A2A agents, and API-first agent services.
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## Quick Start
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```bash
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# Initialize config for your agent
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npx agent-eval init
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# Run evaluation
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ANTHROPIC_API_KEY=your-key npx agent-eval run
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# View results
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cat agent-eval-report/report.json
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```
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## What it does
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`agent-eval` connects to an AI agent, auto-generates test tasks using Claude, executes them, and scores the results across 5 dimensions:
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| Dimension | Weight | What we measure |
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|-----------|--------|-----------------|
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| **Capability** | 30% | Task completion + output quality (LLM-as-judge) |
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| **Reliability** | 25% | Success rate across multiple runs |
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| **Efficiency** | 20% | Response latency |
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| **Safety** | 15% | Prompt injection resistance, scope violations |
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| **Dev Experience** | 10% | Schema quality, error messages |
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## Sample Output
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```
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AgentHunter Eval v0.1.0
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Agent: mcp-memory v1.0.0 (MCP)
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Tools: 9 | Tasks: 18 | Runs: 18
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────────────────────────────────────────────
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SCORE: 76 / 100
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────────────────────────────────────────────
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Capability ███████████░░░░░░░░░ 55%
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Reliability ██████████████████░░ 89%
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Efficiency ████████████████████ 100%
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Safety █████████████░░░░░░░ 67%
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Dev Experience ██████████████░░░░░░ 70%
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```
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## Benchmark Results
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We evaluated 12 popular MCP servers. [See full results →](docs/blog/mcp-server-benchmark.md)
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| Rank | Server | Score | Reliability |
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|------|--------|-------|-------------|
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| 🥇 | mcp-sequential-thinking | **77** | 100% |
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| 🥈 | mcp-datetime | **75** | 73% |
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| 🥉 | context7 | **73** | 100% |
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| 4 | mcp-everything | **68** | 77% |
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| 5 | mcp-filesystem | **62** | 14% |
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Raw evaluation data: [`results/`](results/)
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## Supported Protocols
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- **MCP** (Model Context Protocol) — stdio transport
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- **A2A** — coming in Phase 2
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- **REST API** — coming in Phase 2
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## Development
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```bash
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git clone https://github.com/OrrisTech/agent-eval
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cd agent-eval
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bun install
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bun run --filter agent-eval build
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bun run --filter agent-eval test
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```
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## License
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MIT

‎docs/blog/mcp-server-benchmark.md‎

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---
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title: "We Benchmarked 5 MCP Servers — Here's What We Found"
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date: 2026-04-13
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author: AgentHunter Eval
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---
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# We Benchmarked 5 MCP Servers — Here's What We Found
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The Model Context Protocol (MCP) ecosystem has exploded — over 10,000 servers on the official registry, 97 million monthly SDK downloads. But which MCP servers are actually good?
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We built [agent-eval](https://github.com/OrrisTech/agent-eval), an open-source evaluation framework, and used it to benchmark 12 popular MCP servers across 5 dimensions: **Capability**, **Reliability**, **Efficiency**, **Safety**, and **Developer Experience**.
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Here's what we found.
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## Methodology
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For each server, we:
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1. Connected via stdio transport and discovered all available tools
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2. Used Claude to auto-generate test tasks based on each tool's schema
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3. Executed every task multiple times to measure reliability
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4. Scored output quality using LLM-as-judge (Claude Sonnet 4)
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5. Measured latency, success rate, and safety (prompt injection resistance)
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All evaluation code is [open source](https://github.com/OrrisTech/agent-eval). You can reproduce these results yourself:
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```bash
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npx agent-eval init
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npx agent-eval run
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```
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## Rankings
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| Rank | Server | Category | Score | Capability | Reliability | Efficiency | Safety |
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|------|--------|----------|-------|------------|-------------|------------|--------|
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| 1 🥇 | **mcp-sequential-thinking** | Reasoning | **77** | 33 | 100 | 100 | 100 |
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| 2 🥈 | **mcp-datetime** | Utilities | **75** | 52 | 73 | 100 | 92 |
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| 3 🥉 | **context7** | Search | **73** | 44 | 100 | 88 | 67 |
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| 4 | **mcp-everything** | Reference | **68** | 48 | 77 | 76 | 82 |
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| 5 | **mcp-filesystem** | Filesystem | **62** | 54 | 14 | 100 | 100 |
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## Key Findings
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### 1. Reliability varies wildly
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Of 5 servers tested, 2 achieved 80%+ reliability. However, 1 server(s) fell below 50%: **mcp-filesystem** (14%). Low reliability usually means the server crashes, times out, or returns errors for valid inputs.
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### 2. Efficiency is generally excellent
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Average latency across all servers was 879ms. 3/5 servers scored 90+ on efficiency, meaning sub-second response times. MCP's stdio transport is inherently fast since there's no network overhead.
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### 3. Safety scores reveal gaps
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2/5 servers scored a perfect 100 on safety. 1 scored below 70: **context7** (67). Lower safety scores indicate the server may be susceptible to prompt injection or may expose data outside its intended scope.
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## Individual Results
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### mcp-sequential-thinking
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- **Category**: Reasoning
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- **Score**: 77/100
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- **Tools discovered**: 1
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- **Tasks generated**: 3
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- **Success rate**: 100%
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- **Avg latency**: 2ms
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- **Breakdown**: Cap 33 | Rel 100 | Eff 100 | Safe 100 | DX 70
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### mcp-datetime
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- **Category**: Utilities
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- **Score**: 75/100
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- **Tools discovered**: 10
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- **Tasks generated**: 30
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- **Success rate**: 73%
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- **Avg latency**: 2ms
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- **Breakdown**: Cap 52 | Rel 73 | Eff 100 | Safe 92 | DX 70
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### context7
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- **Category**: Search
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- **Score**: 73/100
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- **Tools discovered**: 2
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- **Tasks generated**: 6
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- **Success rate**: 100%
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- **Avg latency**: 1642ms
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- **Breakdown**: Cap 44 | Rel 100 | Eff 88 | Safe 67 | DX 70
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### mcp-everything
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- **Category**: Reference
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- **Score**: 68/100
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- **Tools discovered**: 13
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- **Tasks generated**: 39
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- **Success rate**: 77%
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- **Avg latency**: 2747ms
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- **Breakdown**: Cap 48 | Rel 77 | Eff 76 | Safe 82 | DX 70
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### mcp-filesystem
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- **Category**: Filesystem
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- **Score**: 62/100
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- **Tools discovered**: 14
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- **Tasks generated**: 42
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- **Success rate**: 14%
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- **Avg latency**: 1ms
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- **Breakdown**: Cap 54 | Rel 14 | Eff 100 | Safe 100 | DX 70
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## Servers That Failed Evaluation
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These servers could not be evaluated (connection failures, crashes, or missing dependencies):
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- **mcp-memory** (Memory): - Loading config...
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✔ Config loaded: mcp-memory (MCP)
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- Connecting to agent via MCP...
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[3
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- **mcp-fetch** (Web): - Loading config...
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✔ Config loaded: mcp-fetch (MCP)
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- Connecting to agent via MCP...
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Err
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- **mcp-git** (DevTools): - Loading config...
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✔ Config loaded: mcp-git (MCP)
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- Connecting to agent via MCP...
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✔
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- **playwright-mcp** (Browser): - Loading config...
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✔ Config loaded: playwright-mcp (MCP)
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- Connecting to agent via MCP...
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- **mcp-puppeteer** (Browser): - Loading config...
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✔ Config loaded: mcp-puppeteer (MCP)
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- Connecting to agent via MCP...
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- **mcp-sqlite** (Database): - Loading config...
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✔ Config loaded: mcp-sqlite (MCP)
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- Connecting to agent via MCP...
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Er
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- **notion-mcp** (Productivity): - Loading config...
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✔ Config loaded: notion-mcp (MCP)
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- Connecting to agent via MCP...
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[3
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## How Scores Are Calculated
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| Dimension | Weight | What we measure |
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|-----------|--------|-----------------|
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| **Capability** | 30% | Task completion rate + output quality (LLM-as-judge) |
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| **Reliability** | 25% | Success rate across multiple runs |
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| **Efficiency** | 20% | Response latency (sub-500ms = 100, >10s = 0) |
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| **Safety** | 15% | Prompt injection resistance, scope violations |
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| **Dev Experience** | 10% | Documentation quality, error messages, schema clarity |
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**Overall Score** = weighted average of all dimensions, scaled to 0-100.
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## Reproduce These Results
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```bash
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git clone https://github.com/OrrisTech/agent-eval
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cd agent-eval
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bun install
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bun run --filter agent-eval build
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# Evaluate a single server
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echo 'agent:
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name: "mcp-memory"
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protocol: mcp
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endpoint: "npx -y @modelcontextprotocol/server-memory"
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capabilities: ["memory"]
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eval:
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runs: 3' > agent-eval.yaml
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ANTHROPIC_API_KEY=your-key npx agent-eval run
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```
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## What's Next
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We're expanding to evaluate A2A agents and REST API agents. If you'd like your MCP server benchmarked, [open an issue](https://github.com/OrrisTech/agent-eval/issues) or submit a PR to our server list.
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---
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*Evaluations run on 2026-04-13 using agent-eval v0.1.0. Scores may vary between runs due to LLM non-determinism. Full raw data available in the [results directory](https://github.com/OrrisTech/agent-eval/tree/main/results).*

‎results/context7/agent-eval.yaml‎

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agent:
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name: "context7"
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version: "1.0.0"
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protocol: mcp
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endpoint: "npx -y @upstash/context7-mcp"
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capabilities:
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- documentation-search
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eval:
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runs: 2

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