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 accessSTORE_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
- Options:
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 accessSTORE_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
License
MIT License