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MCP Evals

A Node.js package and GitHub Action for evaluating MCP (Model Context Protocol) tool implementations using LLM-based scoring, with built-in observability support. This helps ensure your MCP server's tools are working correctly, performing well, and are fully observable with integrated monitoring and metrics.

Installation

As a Node.js Package

npm install mcp-evals

As a GitHub Action

Add the following to your workflow file:

name: Run MCP Evaluations
on:
  pull_request:
    types: [opened, synchronize, reopened]
jobs:
  evaluate:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      pull-requests: write
    steps:
      - uses: actions/checkout@v4
      
      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '20'
          
      - name: Install dependencies
        run: npm install
        
      - name: Run MCP Evaluations
        uses: mclenhard/mcp-evals@v1.0.9
        with:
          evals_path: 'src/evals/evals.ts'    # Can also use .yaml files
          server_path: 'src/index.ts'
          openai_api_key: ${{ secrets.OPENAI_API_KEY }}
          model: 'gpt-4'  # Optional, defaults to gpt-4

Usage -- Evals

1. Create Your Evaluation File

You can create evaluation configurations in either TypeScript or YAML format.

Option A: TypeScript Configuration

Create a file (e.g., evals.ts) that exports your evaluation configuration:

import { EvalConfig } from 'mcp-evals';
import { openai } from "@ai-sdk/openai";
import { grade, EvalFunction} from "mcp-evals";

const weatherEval: EvalFunction = {
    name: 'Weather Tool Evaluation',
    description: 'Evaluates the accuracy and completeness of weather information retrieval',
    run: async () => {
      const result = await grade(openai("gpt-4"), "What is the weather in New York?");
      return JSON.parse(result);
    }
};
const config: EvalConfig = {
    model: openai("gpt-4"),
    evals: [weatherEval]
  };
  
  export default config;
  
  export const evals = [
    weatherEval,
    // add other evals here
]; 

Option B: YAML Configuration

For simpler configuration, you can use YAML format (e.g., evals.yaml):

# Model configuration
model:
  provider: openai     # 'openai' or 'anthropic'
  name: gpt-4o        # Model name
  # api_key: sk-...   # Optional, uses OPENAI_API_KEY env var by default

# List of evaluations to run
evals:
  - name: weather_query_basic
    description: Test basic weather information retrieval
    prompt: "What is the current weather in San Francisco?"
    expected_result: "Should return current weather data for San Francisco including temperature, conditions, etc."

  - name: weather_forecast
    description: Test weather forecast functionality
    prompt: "Can you give me the 3-day weather forecast for Seattle?"
    expected_result: "Should return a multi-day forecast for Seattle"

  - name: invalid_location
    description: Test handling of invalid location requests
    prompt: "What's the weather in Atlantis?"
    expected_result: "Should handle invalid location gracefully with appropriate error message"

2. Run the Evaluations

As a Node.js Package

You can run the evaluations using the CLI with either TypeScript or YAML files:

# Using TypeScript configuration
npx mcp-eval path/to/your/evals.ts path/to/your/server.ts

# Using YAML configuration
npx mcp-eval path/to/your/evals.yaml path/to/your/server.ts

As a GitHub Action

The action will automatically:

1. Run your evaluations
2. Post the results as a comment on the PR
3. Update the comment if the PR is updated

Evaluation Results

Each evaluation returns an object with the following structure:

interface EvalResult {
  accuracy: number;        // Score from 1-5
  completeness: number;    // Score from 1-5
  relevance: number;       // Score from 1-5
  clarity: number;         // Score from 1-5
  reasoning: number;       // Score from 1-5
  overall_comments: string; // Summary of strengths and weaknesses
}

Configuration

Environment Variables

• OPENAI_API_KEY: Your OpenAI API key (required for OpenAI models)
• ANTHROPIC_API_KEY: Your Anthropic API key (required for Anthropic models)

[!NOTE] 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.

Evaluation Configuration

TypeScript Configuration

The EvalConfig interface requires:

• model: The language model to use for evaluation (e.g., GPT-4)
• evals: Array of evaluation functions to run

Each evaluation function must implement:

• name: Name of the evaluation
• description: Description of what the evaluation tests
• run: Async function that takes a model and returns an EvalResult

YAML Configuration

YAML configuration files support:

Model Configuration:

• provider: Either 'openai' or 'anthropic'
• name: Model name (e.g., 'gpt-4o', 'claude-3-opus-20240229')
• api_key: Optional API key (uses environment variables by default)

Evaluation Configuration:

• name: Name of the evaluation (required)
• description: Description of what the evaluation tests (required)
• prompt: The prompt to send to your MCP server (required)
• expected_result: Optional description of expected behavior

Supported File Extensions: .yaml, .yml

Usage -- Monitoring

Note: The metrics functionality is still in alpha. Features and APIs may change, and breaking changes are possible.

1. Add the following to your application before you initilize the MCP server.

import { metrics } from 'mcp-evals';
metrics.initialize(9090, { enableTracing: true, otelEndpoint: 'http://localhost:4318/v1/traces' });

1. Start the monitoring stack:

docker-compose up -d
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