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Gitingest-MCP 
An MCP server for gitingest. This allows MCP clients like Claude Desktop, Cline, Cursor, etc to quickly extract information about Github repositories including
- Repository summaries
- Project directory structure
- File content
https://github.com/user-attachments/assets/c1fa596b-a70b-4d37-91d9-ea5e80284793
Table of Contents
Installation
Installing via Smithery
To install gitingest-mcp via Smithery:
npx -y @smithery/cli@latest install @puravparab/gitingest-mcp --client claude --config "{}" # Claudenpx -y @smithery/cli@latest run @puravparab/gitingest-mcp --client cursor --config "{}" # Cursor ``` ```bash npx -y @smithery/cli@latest install @puravparab/gitingest-mcp --client windsurf --config "{}" # Windsurf ``` ```bash npx -y @smithery/cli@latest install @puravparab/gitingest-mcp --client cline --config "{}" # Cline
Install via Github
Add this to the MCP client config file
{ "mcpServers": { "gitingest-mcp": { "command": "<path to uv>/uvx", "args": [ "--from", "git+https://github.com/puravparab/gitingest-mcp", "gitingest-mcp" ] } } }
Installing Repo Manually
Clone the repo
git clone https://https://github.com/puravparab/Gitingest-MCP cd Gitingest-MCPInstall dependencies
uv syncAdd this to the MCP client config file
{ "mcpServers": { "gitingest": { "command": "<path to uv>/uv", "args": [ "run", "--with", "mcp[cli]", "--with-editable", "<path to gitingest-mcp project>/gitingest_mcp", "mcp", "run", "<path to gitingest-mcp project>/gitingest-mcp/src/gitingest_mcp/server.py" ] } } }If you have issues, follow this MCP server documentation
Updating the MCP client configuration
Add to Claude Desktop
Open config file in your IDE
cursor ~/Library/Application\ Support/Claude/claude_desktop_config.jsoncode ~/Library/Application\ Support/Claude/claude_desktop_config.json
Debug
- Using mcp inspector
uv run mcp dev src/gitingest_mcp/server.py
The mcp-client-for-ollama is a simple yet powerful Python client designed for interacting with Model Context Protocol (MCP) servers using Ollama. This client enables local large language models (LLMs) to utilize tools effectively. It primarily facilitates communication with APIs, streamlining workflows and enhancing the capabilities of LLMs.