GitHub MCP Server (Custom)

A custom Model Context Protocol (MCP) server giving an AI agent full CRUD access to GitHub: repositories, issues, pull requests, branches, comments, reviews, and merges. Built with the official Python mcp SDK (FastMCP) and GitHub's REST API via httpx.

23 tools total — see server.py for the full list. Highlights:

Setup

1. Install dependencies

   pip install -r requirements.txt

2. Create a GitHub token

   Go to https://github.com/settings/tokens and create either:

   • a classic PAT with the repo scope, or

   • a fine-grained PAT with Contents, Issues, Pull requests, and Metadata set to Read & write, scoped to the repos you want the agent to touch.

3. Set the token as an environment variable

   export GITHUB_TOKEN=ghp_your_token_here

4. Run it standalone (optional sanity check)

   python server.py

   It will sit and wait for an MCP client to connect over stdio — that's expected, it's not a web server.

Related MCP server: GitHub MCP Server

Connecting it to Claude Desktop

Add this to your claude_desktop_config.json (%APPDATA%\Claude\claude_desktop_config.json on Windows, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "github": {
      "command": "python",
      "args": ["/absolute/path/to/server.py"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token_here"
      }
    }
  }
}

Restart Claude Desktop and the GitHub tools will show up in the tool picker.

Connecting it to your own agent pipeline (e.g. the Jira → GitHub → Teams project)

Since this is a standard MCP stdio server, any MCP-compatible client or SDK can spawn it as a subprocess and talk to it over stdin/stdout — including a .NET host if you're using the MCP C# SDK, or the Python/TypeScript SDKs if your orchestrator is polyglot. Point the client at:

command: python
args: ["/absolute/path/to/server.py"]
env: { "GITHUB_TOKEN": "<token>" }

Extending it

To add a new tool, just add a new @mcp.tool()-decorated function to server.py that calls _request(method, path, ...) against the GitHub REST API. The docstring becomes the tool's description shown to the model, and type hints become the input schema — so keep both accurate.

Notes

• All write operations (create_*, update_*, delete_*, merge_*) make real changes on GitHub — there's no dry-run mode. Consider testing against a scratch repo first, especially merge_pull_request and delete_branch.

• Rate limits follow GitHub's standard REST API limits (5,000 requests/hour for authenticated requests as of this writing) — the server doesn't do its own throttling.

• Errors from GitHub (bad auth, 404s, merge conflicts, etc.) are raised as RuntimeError with the API's response body included, so the calling agent gets a readable message to react to.