shanksxz gh mcp server

shanksxz gh mcp server avatar

by shanksxz

GitHub context server for AI models. Fetch files, structure, filter, and more.

What is shanksxz gh mcp server

GitHub Repository MCP Server

This Model Context Protocol (MCP) server allows AI models to access GitHub repository contents as context. It provides tools to fetch file contents, repository structure, and entire repositories for use as context in AI interactions.

Features

  • Fetch entire repository contents as context
  • Get specific file contents from a repository
  • Get repository structure (file listing)
  • Filter files by extension
  • Exclude specific paths
  • Limit the number of files returned

Installation

# clone the repository
git clone https://github.com/shanksxz/github-mcp.git
cd github-mcp

# install dependencies
npm install

# build the project
npm run build

Usage

Setting up GitHub Authentication

While the server can work with public repositories without authentication, GitHub API has strict rate limits for unauthenticated requests (60 requests/hour). To increase this limit to 5000 requests/hour, set the GITHUB_TOKEN environment variable:

# create a file called gh.sh and add the following line:
export GITHUB_TOKEN=your_github_personal_access_token
# make the file executable
chmod +x gh.sh
# run the file
./gh.sh

You can create a personal access token in your GitHub Developer Settings.

Using with Cursor

To use this server with Cursor follow these steps:

  1. Open Cursor Settings
  2. Search for "MCP"
  3. Click on "Add a new MCP Server"
  4. Enter the following information:
    • Name: github-repo-context (or any name you want)
    • Type: Command
    • Command: /path/to/your-local-repo-setup/gh.sh
  5. Click "Save"
  6. Enable the server by clicking the toggle next to the server name
  7. You should now be able to use the server in your project

The server communicates via stdin/stdout following the MCP protocol.

Available Tools

The server provides the following tools:

  1. get-repo-context: Get all files from a GitHub repository to use as context

    • Parameters:
      • owner: GitHub repository owner/organization name
      • repo: GitHub repository name
      • maxFiles (optional): Maximum number of files to include (default: 50)
      • fileExtensions (optional): File extensions to include (e.g., ['js', 'ts', 'md'])
      • excludePaths (optional): Paths to exclude (default: ['node_modules', 'dist', 'build'])
  2. get-file-content: Get content of a specific file from a GitHub repository

    • Parameters:
      • owner: GitHub repository owner/organization name
      • repo: GitHub repository name
      • path: Path to the file in the repository
  3. get-repo-structure: Get the structure of a GitHub repository

    • Parameters:
      • owner: GitHub repository owner/organization name
      • repo: GitHub repository name

Example

When integrated with an AI model that supports MCP, you can use commands like:

Get the structure of the repository tensorflow/tensorflow

The AI would then use the get-repo-structure tool to fetch and display the repository structure.

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Frequently Asked Questions

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.

What are MCP Servers?

MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.

How do MCP Servers work?

MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.

Are MCP Servers secure?

Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.

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