MCP Evals: A Deep Dive into Evals in MCP

As LLMs power more products, systematic evaluation becomes essential to ensure reliability and user satisfaction. Evals—short for evaluations—are structured processes that measure how well a model meets predefined criteria. This article explores what evals are, why they matter, and how to design and run them effectively.

What Are Evals?

Evals refer to the assessments, both automated and human-driven, that quantify an AI model's performance on specific tasks. Unlike traditional unit tests, evals capture qualitative aspects such as:

  • Accuracy: Does the model produce correct information?
  • Completeness: Does it cover all necessary details?
  • Relevance: Is the output appropriate for the user's query?
  • Clarity: Is the response easy to understand?
  • Reasoning: Does the model demonstrate logical steps?

Evals help answer questions like:

How often does the model hallucinate facts?

Is the tone suitable for the target audience?

Can the model consistently use external tools via protocols like MCP?

Why Evals Matter for MCP

In MCP (Model Context Protocol) workflows, AI assistants rely on external tools exposed by servers to perform tasks (MCP Introduction). Evals ensure these tool integrations work correctly and consistently by:

  • Verifying correct tool selection: Evals check that the model invokes the appropriate tool for each query.
  • Validating input schemas: They confirm tool arguments conform to the required JSON schema, preventing runtime errors (MCP Tools).
  • Testing error handling: Evals assess how the model and server respond to invalid inputs or tool failures.
  • Measuring end-to-end behavior: They evaluate the entire MCP round-trip, from tool discovery to invocation results.

Types of Evals

Evals generally fall into three main categories:

  1. Human Evaluations

    Subject-matter experts or end users rate outputs using thumbs-up/down, numerical scales, or feedback forms. This yields high-quality judgments but can be costly and sparse.

  2. Code-Based Evaluations

    Automated scripts check outputs against objective criteria—for example, verifying that generated code compiles without errors or that JSON outputs match a schema. These evals are fast and repeatable but may miss subtleties.

  3. LLM-Based Evaluations

    A secondary "judge" model scores or labels outputs based on prompts. This approach offers scalable, detailed feedback at lower cost, though it requires careful prompt design and calibration.

Designing an Effective Eval

Each eval typically follows four steps:

  1. Set the Role: Define the judge's perspective (e.g., "You are a technical reviewer.").
  2. Provide Context: Supply the model output and any supporting information.
  3. Specify the Goal: Clearly articulate what success looks like (e.g., "Rate factual accuracy on a scale of 0–1.").
  4. Define Labels: Explain the scoring rubric or categories.

Example: Automated Accuracy Eval 

npm install mcp-evals

Creating Evaluations

Here's a simple example of how to create an evaluation via the mcp evals package (I'm the author).

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 Evals in CI 

name: AI Evals
  on: [push]
  jobs:
    evaluate:
      runs-on: ubuntu-latest
      steps:
        - uses: actions/checkout@v3
        - name: Install Dependencies
          run: npm install mcp-evals openai
        - name: Run Evals
          run: npx mcp-eval src/evals.ts src/server.ts

For full documentation on how to integrate evals into your mcp server see the documentation here.

Best Practices

  • Start simple: Begin with a few core evals before scaling up.
  • Mix eval types: Combine human, code-based, and LLM-based methods.
  • Iterate on prompts: Refine your judge-prompts to reduce ambiguity.
  • Track trends: Use dashboards to monitor eval scores over time.

Conclusion

Evals are the backbone of reliable AI applications. By defining clear criteria and automating assessments, you can detect regressions early, maintain quality, and build user trust. Whether you write human surveys, code checks, or LLM-judge prompts, systematic evals ensure your AI delivers on its promises.