Kedasha Kerr explains how GitHub’s newly open-sourced MCP server bridges AI tools and GitHub, enabling rich, automated workflows and natural-language interactions for developers.

Why We Open Sourced Our MCP Server and What It Means for Developers

By Kedasha Kerr

Introduction

This article dives into why GitHub open-sourced its Model Context Protocol (MCP) server, how it facilitates natural language-powered workflows between AI tools and GitHub, and practical ways developers can use it today.

The Problem: AI Models Need Accurate, Real-Time Context

AI developers using tools like VS Code’s Copilot agent often encounter limitations—such as models hallucinating outdated information due to a lack of direct system context. Without a robust way for large language models (LLMs) to connect to external software and live data, the value of AI is constrained.

The Solution: Model Context Protocol (MCP)

The MCP is an open protocol that standardizes connections between LLM applications and external data sources/tools. Inspired by the Language Server Protocol (LSP), MCP uses a client-server architecture for clearer, more reliable communication between AI apps and systems like GitHub.

Key Concepts

  • MCP Host: The AI application (e.g., Copilot Chat, VS Code).
  • MCP Client: Runs inside the host and talks to MCP servers.
  • MCP Server: Exposes capabilities and data endpoints via the MCP protocol.

GitHub’s MCP Server: Architecture and Use Cases

GitHub’s open-source MCP server provides a universal, MCP-compliant interface between AI tools and GitHub. Instead of relying on direct REST/GraphQL API calls, developers can:

  • List open issues
  • Show pull requests waiting for review
  • Fetch file or repo metadata
  • Create or comment on issues

The server translates natural language requests into structured API actions, enabling real-time, reliable data and true workflow automation.

How It Works

  1. Server: Runs standalone, handling MCP requests.
  2. Client: Bridges the host’s natural language intent into MCP protocol.
  3. Host: Surfaces conversational or code-driven workflows (e.g., in an IDE or chat UI).

Flow: User asks question → Host forms semantic request → Client encodes as MCP → Server interacts with GitHub → Returns structured JSON.

This clear separation of concerns makes each layer modular, adaptable, and easy to test or swap out.

Getting Started

To use the GitHub MCP server in VS Code:

  1. Add the server configuration:

    { "servers": { "github": { "type": "http", "url": "https://api.githubcopilot.com/mcp/" } } }
  2. Create a /vscode/mcp.json file in your project and paste the configuration.
  3. Start the server and complete OAuth as prompted in VS Code.

Now, VS Code and compatible tools can fetch GitHub context seamlessly with natural language.

Real-World Automation Examples

  • Markdown Automation: Transforming GitHub issues into Markdown files for websites, automating what was once manual work.
  • Weekly Team Digests: Bots that summarize repo activity (PRs, issues, merges) and post to Slack.
  • Conversational Project Assistants: Chat interfaces for contributors to query project status or release changes in plain English.
  • Personal LLM Dashboards: Custom dashboards offering daily prompts, reminders, and release note drafts for maintainers.

Further Resources

Conclusion

With MCP and the open-source MCP server, developers can build smarter AI-powered tools and automate complex GitHub workflows using natural language interfaces. The result is richer developer experiences and more reliable, context-aware automation.

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