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This helps ensure your MCP server's tools are working correctly, performing well, and are fully observable with integrated monitoring and metrics.\n\n## Installation\n\n### As a Node.js Package\n\n```bash\nnpm install mcp-evals\n```\n\n### As a GitHub Action\n\nAdd the following to your workflow file:\n\n```yaml\nname: Run MCP Evaluations\non:\n  pull_request:\n    types: [opened, synchronize, reopened]\njobs:\n  evaluate:\n    runs-on: ubuntu-latest\n    permissions:\n      contents: read\n      pull-requests: write\n    steps:\n      - uses: actions/checkout@v4\n      \n      - name: Setup Node.js\n        uses: actions/setup-node@v4\n        with:\n          node-version: '20'\n          \n      - name: Install dependencies\n        run: npm install\n        \n      - name: Run MCP Evaluations\n        uses: mclenhard/mcp-evals@v1.0.9\n        with:\n          evals_path: 'src/evals/evals.ts'    # Can also use .yaml files\n          server_path: 'src/index.ts'\n          openai_api_key: ${{ secrets.OPENAI_API_KEY }}\n          model: 'gpt-4'  # Optional, defaults to gpt-4\n```\n\n## Usage -- Evals\n\n### 1. Create Your Evaluation File\n\nYou can create evaluation configurations in either TypeScript or YAML format.\n\n#### Option A: TypeScript Configuration\n\nCreate a file (e.g., `evals.ts`) that exports your evaluation configuration:\n\n```typescript\nimport { EvalConfig } from 'mcp-evals';\nimport { openai } from \"@ai-sdk/openai\";\nimport { grade, EvalFunction} from \"mcp-evals\";\n\nconst weatherEval: EvalFunction = {\n    name: 'Weather Tool Evaluation',\n    description: 'Evaluates the accuracy and completeness of weather information retrieval',\n    run: async () => {\n      const result = await grade(openai(\"gpt-4\"), \"What is the weather in New York?\");\n      return JSON.parse(result);\n    }\n};\nconst config: EvalConfig = {\n    model: openai(\"gpt-4\"),\n    evals: [weatherEval]\n  };\n  \n  export default config;\n  \n  export const evals = [\n    weatherEval,\n    // add other evals here\n]; \n```\n\n#### Option B: YAML Configuration\n\nFor simpler configuration, you can use YAML format (e.g., `evals.yaml`):\n\n```yaml\n# Model configuration\nmodel:\n  provider: openai     # 'openai' or 'anthropic'\n  name: gpt-4o        # Model name\n  # api_key: sk-...   # Optional, uses OPENAI_API_KEY env var by default\n\n# List of evaluations to run\nevals:\n  - name: weather_query_basic\n    description: Test basic weather information retrieval\n    prompt: \"What is the current weather in San Francisco?\"\n    expected_result: \"Should return current weather data for San Francisco including temperature, conditions, etc.\"\n\n  - name: weather_forecast\n    description: Test weather forecast functionality\n    prompt: \"Can you give me the 3-day weather forecast for Seattle?\"\n    expected_result: \"Should return a multi-day forecast for Seattle\"\n\n  - name: invalid_location\n    description: Test handling of invalid location requests\n    prompt: \"What's the weather in Atlantis?\"\n    expected_result: \"Should handle invalid location gracefully with appropriate error message\"\n```\n\n### 2. Run the Evaluations\n\n#### As a Node.js Package\n\nYou can run the evaluations using the CLI with either TypeScript or YAML files:\n\n```bash\n# Using TypeScript configuration\nnpx mcp-eval path/to/your/evals.ts path/to/your/server.ts\n\n# Using YAML configuration\nnpx mcp-eval path/to/your/evals.yaml path/to/your/server.ts\n```\n\n#### As a GitHub Action\n\nThe action will automatically:\n1. Run your evaluations\n2. Post the results as a comment on the PR\n3. Update the comment if the PR is updated\n\n## Evaluation Results\n\nEach evaluation returns an object with the following structure:\n\n```typescript\ninterface EvalResult {\n  accuracy: number;        // Score from 1-5\n  completeness: number;    // Score from 1-5\n  relevance: number;       // Score from 1-5\n  clarity: number;         // Score from 1-5\n  reasoning: number;       // Score from 1-5\n  overall_comments: string; // Summary of strengths and weaknesses\n}\n```\n\n## Configuration\n\n### Environment Variables\n\n- `OPENAI_API_KEY`: Your OpenAI API key (required for OpenAI models)\n- `ANTHROPIC_API_KEY`: Your Anthropic API key (required for Anthropic models)\n\n> [!NOTE]\n> If you're using this GitHub Action with open source software, enable data sharing in the OpenAI billing dashboard to claim 2.5 million free GPT-4o mini tokens per day, making this Action effectively free to use.\n\n### Evaluation Configuration\n\n#### TypeScript Configuration\n\nThe `EvalConfig` interface requires:\n\n- `model`: The language model to use for evaluation (e.g., GPT-4)\n- `evals`: Array of evaluation functions to run\n\nEach evaluation function must implement:\n\n- `name`: Name of the evaluation\n- `description`: Description of what the evaluation tests\n- `run`: Async function that takes a model and returns an `EvalResult`\n\n#### YAML Configuration\n\nYAML configuration files support:\n\n**Model Configuration:**\n- `provider`: Either 'openai' or 'anthropic'\n- `name`: Model name (e.g., 'gpt-4o', 'claude-3-opus-20240229')\n- `api_key`: Optional API key (uses environment variables by default)\n\n**Evaluation Configuration:**\n- `name`: Name of the evaluation (required)\n- `description`: Description of what the evaluation tests (required)\n- `prompt`: The prompt to send to your MCP server (required)\n- `expected_result`: Optional description of expected behavior\n\n**Supported File Extensions:** `.yaml`, `.yml`\n\n## Usage -- Monitoring\n\n> **Note:** The metrics functionality is still in alpha. Features and APIs may change, and breaking changes are possible.\n\n1. Add the following to your application before you initilize the MCP server. \n\n```typescript \nimport { metrics } from 'mcp-evals';\nmetrics.initialize(9090, { enableTracing: true, otelEndpoint: 'http://localhost:4318/v1/traces' });\n```\n\n2. Start the monitoring stack:\n\n```bash\ndocker-compose up -d\n```\n\n3. Run your MCP server and it will automatically connect to the monitoring stack.\n\n### Accessing the Dashboards\n\n- **Prometheus**: http://localhost:9090\n- **Grafana**: http://localhost:3000 (username: admin, password: admin)\n- **Jaeger UI**: http://localhost:16686\n\n## Metrics Available\n\n- **Tool Calls**: Number of tool calls by tool name\n- **Tool Errors**: Number of errors by tool name\n- **Tool Latency**: Distribution of latency times by tool name\n\n\n## License\n\nMIT \n","readmeFilename":"README.md"}