Understanding MCP Evals: Why Evals Matter for MCP

As AI tools become increasingly integrated into our daily workflows, ensuring their reliability and performance is paramount. Today, I want to dive into MCP Evals, a solution I've been working with that helps developers evaluate Model Context Protocol (MCP) implementations effectively.

What is MCP?

Before we get into evals, let's clarify what MCP actually is. The Model Context Protocol (MCP) is a standardized way for AI models to interact with external tools and functions. It enables AI assistants to perform actions like searching the web, accessing databases, or manipulating files on a user's behalf.

When an AI assistant needs to perform an action beyond generating text, MCP provides the structure for how the AI can request that action and receive results. This capability is what allows modern AI assistants to not just talk about doing things, but to actually do them.

What is an "Eval"?

In the AI development lifecycle, an "eval" (short for evaluation) is a critical phase that helps teams understand if their AI model is actually doing what they want it to do. Unlikes traditional unit testing evals are a great way to answer qualitative questions like:

• How well is the model answering the question ?
• How thorough is the answer ?
• Is how relevant is the answer to the question ?

For MCP implementations specifically, evaluations help ensure that the tools provided to AI models function correctly, consistently, and with high performance.

Why MCP Evals Matter

When building AI systems that leverage external tools via MCP, the reliability of those tool connections becomes critical to the overall system's success. If an AI assistant can't properly access or use the tools it needs, the user experience suffers dramatically.

Imagine asking an AI assistant to check the weather, only to receive incorrect information or for the client to choose the wrong tool entirely.

This is where MCP Evals comes in — it provides a standardized, automated way to test and evaluate MCP tool implementations.

Introducing the MCP Evals Package

I developed MCP Evals as a Node.js package and GitHub Action to streamline the evaluation process. It uses LLM-based scoring (leveraging models like GPT-4) to assess how well your MCP tools are performing.

How It Works

The evaluation process is straightforward:

1. You create evaluation scenarios relevant to your MCP tools
2. The evaluation runs those scenarios against your MCP server
3. An LLM grades the responses based on predefined criteria
4. You receive detailed scores and feedback to improve your implementation

Each evaluation provides scores from 1-5 on several key metrics:

• Accuracy: How correct is the information provided?
• Completeness: Does it provide all necessary information?
• Relevance: Is the response appropriate for the query?
• Clarity: Is the information presented clearly?
• Reasoning: Does the model show sound reasoning in its use of tools?

Getting Started with MCP Evals

I've designed the package to be easy to use either as a Node.js library or a GitHub Action.

Installation

npm install mcp-evals

Creating Evaluations

Here's a simple example of how to create an evaluation:

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;

Running Evaluations

You can run evaluations using the CLI:

npx mcp-eval path/to/your/evals.ts path/to/your/server.ts

Or integrate it into your GitHub workflow:

- name: Run MCP Evaluations
  uses: mclenhard/[email protected]
  with:
    evals_path: 'src/evals/evals.ts'
    server_path: 'src/index.ts'
    openai_api_key: ${{ secrets.OPENAI_API_KEY }}
    model: 'gpt-4'

Benefits of Continuous Evaluation

Integrating MCP Evals into your development workflow offers several key benefits:

1. Early detection of issues: Catch problems before they reach users
2. Objective measurement: Get consistent, quantifiable metrics on tool performance
3. Continuous improvement: Track how changes impact your tools' effectiveness
4. Quality assurance: Ensure your AI features meet quality standards before release

Final Thoughts

As AI assistants increasingly rely on external tools to provide value, ensuring those tool connections work flawlessly becomes essential. MCP Evals provides a systematic approach to evaluating and improving your MCP implementations.

By incorporating these evaluations into your development process, you can build more reliable, effective AI features that truly deliver on their promises.

If you're building with MCP, I encourage you to try MCP Evals and see how it can help improve your implementations. The package is available on npm and GitHub under an MIT license.