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GitHub MCP Server Integration

@faheema15

关于 GitHub MCP Server Integration

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python

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stdio

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faheema15

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概览

What is GitHub MCP Server Integration?

A Model Context Protocol (MCP) server that integrates with the GitHub API. It allows AI assistants like Claude or OpenAI GPT to fetch user profiles, list repositories, and create issues on GitHub using natural language.

How to use GitHub MCP Server Integration?

Clone the repository, install dependencies, set a GitHub personal access token in a .env file as GITHUB_TOKEN, then run uvicorn main:app --reload for local development. Alternatively, use the deployed server at https://github-mcp-server-production.up.railway.app. Register the .well-known/ai-plugin.json endpoint with an AI assistant to enable interaction.

Key features of GitHub MCP Server Integration

  • MCP-compliant server with OpenAPI documentation
  • Secure GitHub interaction using personal access token
  • Fetch GitHub user details
  • List repositories by username
  • Create issues on repositories

Use cases of GitHub MCP Server Integration

  • AI assistant retrieves authenticated user’s GitHub profile
  • AI assistant lists repositories for a given GitHub username
  • AI assistant creates an issue in a specified repository

FAQ from GitHub MCP Server Integration

How do I authenticate with GitHub?

Set a GitHub Personal Access Token (PAT) with repo permissions as the environment variable GITHUB_TOKEN in your .env file or in Railway’s environment settings.

What endpoints does the server expose?

Three endpoints: GET /github/user (authenticated user info), GET /github/repos/{username} (list repos), and POST /github/repos/{owner}/{repo}/issues (create an issue).

How can I deploy the server?

Deploy on Railway: create a new project from your GitHub repo, add the GITHUB_TOKEN environment variable, and set the start command to uvicorn main:app --host 0.0.0.0 --port 8000.

What are the local development prerequisites?

Python 3.9+, a GitHub PAT with repo permissions, and cloned repo. Install dependencies with pip install -r requirements.txt, then run uvicorn main:app --reload.

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