Ksenia Bobrova explains how the GitHub MCP Server team employs rigorous offline evaluation methods to improve GitHub Copilot’s integration with AI models, ensuring high-quality, accurate developer workflows.

Measuring What Matters: Offline Evaluation of GitHub MCP Server

Author: Ksenia Bobrova

Introduction

Model Context Protocol (MCP) is a standardized way for AI models (particularly large language models, or LLMs) to interact with APIs and data—like a universal protocol that facilitates seamless integration between models and services. The GitHub MCP Server implements this protocol, serving as a foundation for various GitHub Copilot workflows inside and outside GitHub.

Ensuring that new features and improvements don’t introduce regressions or reduce tool quality is crucial. This is where automated offline evaluation pipelines provide value, enabling rapid, concrete feedback on changes and helping teams iterate confidently.

What is the Model Context Protocol and GitHub MCP Server?

  • MCP: An interface allowing AI models to communicate with APIs and services by exposing a catalog of ‘tools,’ each with defined actions and parameters.
  • GitHub MCP Server: Offers these tools to GitHub Copilot and other models, listing their capabilities and parameters in a way models can reliably use.

Why Tool Descriptions Matter

Small changes in how tools or their parameters are described can have a significant impact. Subtle wording tweaks may alter how reliably a model chooses and uses a tool, affecting overall workflow outcomes. Safe, iterative improvement hinges on detecting whether changes help or harm model behavior before deployment.

Automated Offline Evaluation Pipeline

The GitHub MCP Server engineering team uses a multi-stage offline evaluation pipeline:

1. Fulfillment

  • Each benchmark request (user input) is run through different models and toolsets.
  • The model’s tool choices and supplied arguments are logged.

2. Evaluation

  • Metrics and statistics are computed on the results.
  • The focus is on two questions:
    1. Did the model select the correct tool(s)?
    2. Did it supply the correct arguments?

3. Summarization

  • Aggregate per-dataset and per-tool metrics into comprehensive reports.

Benchmark Structure

  • Input: Natural language user request
  • Expected tools: List of expected tool invocations
  • Expected arguments: The precise parameters those tools require

Example Benchmarks

  • Counting issues in a repo for a specific month
  • Merging pull requests with specific methods and titles
  • Requesting code reviews by user
  • Summarizing comments in a discussion thread

Evaluation Metrics and Algorithms

Tool Selection

For benchmarks involving one tool, tool selection is a multi-class classification problem:

  • Accuracy: % of correct tool selections
  • Precision: % of correct tool uses among all uses
  • Recall: % of times the right tool got picked
  • F1-score: Harmonic mean of precision and recall

A confusion matrix is used to analyze tool confusion (e.g., distinguishing list_issues from search_issues).

Example Confusion Matrix

Expected tool / Called tool search_issues list_issues
search_issues 7 3
list_issues 0 10

Argument Correctness

Metrics include:

  • Argument hallucination (extra, unintended arguments)
  • All expected arguments provided
  • All required arguments provided
  • Exact value match (argument values match expectations)

Future Directions

  • Expanding benchmark coverage: Larger datasets mean more reliable outcomes.
  • Multi-tool flow evaluation: Moving from single-tool to sequential, dependency-based workflows requires more advanced analysis, like multi-label classification.
  • Enhanced summarization: Better aggregation will yield clearer insights.

Key Takeaways

  • Offline evaluation allows for safe, iterative improvement of MCP workflows and AI-powered developer tools.
  • Comprehensive metrics on both tool selection and argument accuracy lead to actionable improvements and fewer regressions.
  • The pipeline is being extended to better handle complex, multi-tool flows and larger, more representative benchmarks.

References

By focusing on concrete, automated feedback, the GitHub MCP Server team ensures Copilot and similar integrations become more accurate, productive, and reliable for developers.

This post appeared first on “The GitHub Blog”. Read the entire article here