iflow-mcp_sui-mcp-server

# MCP Server with FAISS for RAG This project provides a proof-of-concept implementation of a Machine Conversation Protocol (MCP) server that allows an AI agent to query a vector database and retrieve relevant documents for Retrieval-Augmented Generation (RAG). ## Features - FastAPI server with MCP endpoints - FAISS vector database integration - Document chunking and embedding - GitHub Move file extraction and processing - LLM integration for complete RAG workflow - Simple client example - Sample documents ## Installation ### Using pipx (Recommended) [pipx](https://pypa.github.io/pipx/) is a tool to help you install and run Python applications in isolated environments. 1. First, install pipx if you don't have it: ```bash # On macOS brew install pipx pipx ensurepath # On Ubuntu/Debian sudo apt update sudo apt install python3-pip python3-venv python3 -m pip install --user pipx python3 -m pipx ensurepath # On Windows with pip pip install pipx pipx ensurepath ``` 2. Install the MCP Server package directly from the project directory: ```bash # Navigate to the directory containing the mcp_server folder cd /path/to/mcp-server-project # Install in editable mode pipx install -e . ``` 3. (Optional) Configure environment variables: - Copy `.env.example` to `.env` - Add your GitHub token for higher rate limits: `GITHUB_TOKEN=your_token_here` - Add your OpenAI or other LLM API key for RAG integration: `OPENAI_API_KEY=your_key_here` ### Manual Installation If you prefer not to use pipx: 1. Clone the repository 2. Install dependencies: ```bash cd mcp_server pip install -r requirements.txt ``` ## Usage with pipx After installing with pipx, you'll have access to the following commands: ### Downloading Move Files from GitHub ```bash # Download Move files with default settings mcp-download --query "use sui" --output-dir docs/move_files # Download with more options mcp-download --query "module sui::coin" --max-results 50 --new-index --verbose ``` ### I…