Gemini RAG MCP Server

A Model Context Protocol (MCP) server that provides RAG (Retrieval-Augmented Generation) capabilities using Google's Gemini API File Search feature. This server enables AI applications to create knowledge bases and retrieve information from uploaded documents.

Features

• ✅ File Search RAG: Create and manage knowledge bases using Gemini's File Search API

• ✅ Document Upload: Upload files and text content to create searchable knowledge bases

• ✅ Information Retrieval: Query knowledge bases to retrieve relevant information

• ✅ Configurable Models: Choose Gemini models via environment variable

• ✅ MCP Protocol: Full compatibility with Model Context Protocol

• ✅ Type-Safe: Full TypeScript support with strict mode enabled

• ✅ Dual Transport Support: stdio (default) and HTTP transports

• ✅ Production-Ready: Logging, error handling, and configuration management

Prerequisites

• Node.js >= 22.10.0

• pnpm >= 10.19.0

• Google API Key with Gemini API access

Installation

Using with Claude Desktop (Recommended)

Add the following to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "gemini-rag-mcp": {
      "command": "npx",
      "args": ["-y", "@r_masseater/gemini-rag-mcp"],
      "env": {
        "GOOGLE_API_KEY": "your_google_api_key_here",
        "STORE_DISPLAY_NAME": "your_store_name"
      }
    }
  }
}

Required Environment Variables:

• GOOGLE_API_KEY: Your Google API key with Gemini API access

• STORE_DISPLAY_NAME: Display name for your vector store/knowledge base

Optional Environment Variables:

• GEMINI_MODEL: Gemini model to use for queries (default: gemini-2.5-pro)

  • Options: gemini-2.5-pro, gemini-2.5-flash

After configuration, restart Claude Desktop to load the server.

Development

1. Clone the repository

git clone https://github.com/masseater/gemini-rag-mcp.git
cd gemini-rag-mcp

2. Install dependencies

pnpm install

3. Run in development mode

# stdio transport (default)
pnpm run dev

# HTTP transport (with hot reload)
pnpm run dev:http

Environment Variables

Required:

• GOOGLE_API_KEY: Google API key with Gemini API access

• STORE_DISPLAY_NAME: Display name for vector store/knowledge base

Optional:

• GEMINI_MODEL: Gemini model for queries (default: gemini-2.5-pro)

• LOG_LEVEL: Logging level (error|warn|info|debug, default: info)

• DEBUG: Enable debug console output (true|false, default: false)

• PORT: HTTP server port (default: 3000)

Available Tools

Once configured with Claude Desktop, the following tools are available:

• upload_file: Upload document files to the knowledge base

• upload_content: Upload text content directly to the knowledge base

• query: Query the knowledge base using RAG

Resources

• Model Context Protocol Documentation

• Gemini API Documentation

License

MIT License