gemini-mcp-server

lbds137/gemini-mcp-server

3.1

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The Gemini MCP Server is a Model Context Protocol server that facilitates collaboration between Claude and Google's Gemini AI models.

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Gemini MCP Server

A Model Context Protocol (MCP) server that enables Claude to collaborate with Google's Gemini AI models.

Features

  • 🤖 Multiple Gemini Tools: Ask questions, review code, brainstorm ideas, generate tests, and get explanations
  • 🔄 Dual-Model Support: Automatic fallback from experimental to stable models
  • ⚡ Configurable Models: Easy switching between different Gemini variants
  • 🛡️ Reliable: Never lose functionality with automatic model fallback
  • 📊 Transparent: Shows which model was used for each response

Quick Start

1. Prerequisites

2. Installation

# Clone the repository
git clone https://github.com/lbds137/gemini-mcp-server.git
cd gemini-mcp-server

# Install dependencies
pip install -r requirements.txt

# Copy and configure environment
cp .env.example .env
# Edit .env and add your GEMINI_API_KEY

3. Configuration

Edit .env to configure your models:

# Your Gemini API key (required)
GEMINI_API_KEY=your_api_key_here

# Model configuration (optional - defaults shown)
GEMINI_MODEL_PRIMARY=gemini-2.5-pro-preview-06-05
GEMINI_MODEL_FALLBACK=gemini-1.5-pro
GEMINI_MODEL_TIMEOUT=10000

4. Development Setup

For development with PyCharm or other IDEs:

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e .

# Run tests
python -m pytest

5. Register with Claude

# Install to MCP location
./scripts/install.sh

# Or manually register
claude mcp add gemini-collab python3 ~/.claude-mcp-servers/gemini-collab/server.py

Available Tools

ask_gemini

General questions and problem-solving assistance.

gemini_code_review

Get code review feedback focusing on security, performance, and best practices.

gemini_brainstorm

Collaborative brainstorming for architecture and design decisions.

gemini_test_cases

Generate comprehensive test scenarios for your code.

gemini_explain

Get clear explanations of complex code or concepts.

server_info

Check server status and model configuration.

Model Configurations

Best Quality (Default)

GEMINI_MODEL_PRIMARY=gemini-2.5-pro-preview-06-05
GEMINI_MODEL_FALLBACK=gemini-1.5-pro

Best Performance

GEMINI_MODEL_PRIMARY=gemini-2.5-flash-preview-05-20
GEMINI_MODEL_FALLBACK=gemini-2.0-flash

Most Cost-Effective

GEMINI_MODEL_PRIMARY=gemini-2.0-flash
GEMINI_MODEL_FALLBACK=gemini-2.0-flash-lite

Development

Project Structure

gemini-mcp-server/
├── src/
│   └── gemini_mcp/
│       ├── __init__.py
│       └── server.py      # Main server with DualModelManager
├── tests/
│   └── test_server.py
├── scripts/
│   ├── install.sh       # Quick installation script
│   ├── install.sh       # Install/update deployment script
│   └── dev-link.sh      # Development symlink script
├── docs/
│   └── BUILD_YOUR_OWN_MCP_SERVER.md
├── .claude/
│   └── settings.json    # Claude Code permissions
├── .env                 # Your configuration (git-ignored)
├── .env.example         # Example configuration
├── .gitignore
├── CLAUDE.md           # Instructions for Claude Code
├── LICENSE
├── README.md           # This file
├── docs/
│   ├── BUILD_YOUR_OWN_MCP_SERVER.md
│   ├── DUAL_MODEL_CONFIGURATION.md # Dual-model setup guide
│   ├── PYCHARM_SETUP.md
│   └── TESTING.md
├── requirements.txt
├── setup.py
├── package.json        # MCP registration metadata
└── package-lock.json

Running Tests

python -m pytest tests/ -v

Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

Updating

To update your local MCP installation after making changes:

./scripts/install.sh

This script intelligently handles both installation and updates.

Troubleshooting

Server not found

# Check registration
claude mcp list

# Re-register if needed
./scripts/install.sh

API Key Issues

# Verify environment variable
echo $GEMINI_API_KEY

# Test directly
python -c "import google.generativeai as genai; genai.configure(api_key='$GEMINI_API_KEY'); print('✅ API key works')"

Model Availability

Some models may not be available in all regions. Check the fallback model in logs if primary fails consistently.

License

MIT License - see file for details.

Acknowledgments