gyasis/gemini-mcp

Claude-Gemini MCP Server

Gemini MCP Server

This project provides a server that implements the Multi-tool Co-pilot Protocol (MCP), enabling a primary AI model to collaborate with Google's Gemini Pro model for enhanced capabilities.

It acts as a bridge, allowing a compatible AI assistant to offload specific tasks like asking questions, reviewing code, or brainstorming to Gemini.

Features

• General MCP Interface: Implements the MCP protocol for easy integration with any compatible client.
• Gemini Integration: Leverages the power of Google's Gemini 2.0 Flash model.
• Multi-Image Analysis: Compare and analyze up to 3,600 images in a single request.
• Extensible Toolset: Easily add new tools that call upon Gemini's capabilities.
• Multimodal Support: Process text, images, and videos through Gemini's multimodal capabilities.
• Lightweight and Fast: Built with Python, easy to run and deploy.

Setup and Installation

This project uses uv for fast and reliable Python environment and package management.

1. Install uv: If you don't have uv installed, follow the official installation instructions:

   # On macOS and Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Configure API Key: Create a .env file in the root of the project and add your Gemini API key:

   GEMINI_API_KEY="YOUR_API_KEY_HERE"
   

Running the Server

With uv installed and your .env file created, you can run the server with a single command. uv will automatically create a virtual environment and install the dependencies from requirements.txt for you.

uv run python server.py

The server will start and listen for requests from any MCP-compatible client.

Development and Testing

Using MCP Dev Server

For development and testing, you can use the built-in MCP development server:

# Run the development server
uv run mcp dev server.py

This provides enhanced debugging output and development features.

Testing with MCP Inspector

The MCP Inspector provides a web-based interface to test your server tools interactively:

# Install and run MCP Inspector
npx @modelcontextprotocol/inspector

Then open the provided URL (usually http://localhost:6274) in your browser. You can:

• Test all available tools
• View tool schemas and descriptions
• Execute tools with custom parameters
• Monitor server responses in real-time

To connect your server to the inspector:

1. In the inspector web interface, add a new server
2. Use these connection details:
   • Command: uv
   • Args: ["run", "python", "server.py"]
   • Working Directory: /path/to/your/claude_code-gemini-mcp

Direct Testing

You can also test the server directly:

# Test server startup
uv run python server.py

# The server will wait for JSON-RPC input on stdin
# Press Ctrl+C to exit

Client Integration

To integrate this server with an MCP-compatible client, you need to generate a JSON configuration. The included script makes this easy.

Run the script to generate the necessary JSON:

./generate_config.sh

This will output a JSON block that you can add to your client's configuration file. The output will look something like this, with the correct absolute paths for your system:

{
  "claude_code-gemini-mcp": {
    "name": "Gemini MCP Server",
    "description": "A general MCP server for Gemini integration.",
    "command": "/path/to/your/project/claude_code-gemini-mcp/.venv/bin/python",
    "args": ["/path/to/your/project/claude_code-gemini-mcp/server.py"],
    "env": {},
    "enabled": true
  }
}

How it Works

The server follows the Multi-tool Co-pilot Protocol (MCP), a spec for how an AI assistant can talk to tools.

• initialize: The client starts communication and the server returns its capabilities.
• tools/list: The client requests a list of available tools.
• tools/call: The client asks the server to run a specific tool with given arguments.

The server currently exposes the following tools to the client:

• ask_gemini: Ask Gemini a direct question.
• gemini_code_review: Get a code review from Gemini.
• gemini_brainstorm: Brainstorm ideas with Gemini.
• gemini_debug: Analyze error messages and suggest fixes.
• gemini_research: Get research with Google Search grounding.
• watch_video: Analyze YouTube videos or local video files.
• interpret_image: Analyze one or multiple images (supports up to 3,600 images per request for comparison and batch analysis).