🤖 MCP Server Gemini

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A state-of-the-art Model Context Protocol (MCP) server that provides seamless integration with Google's Gemini AI models. This server enables Claude Desktop and other MCP-compatible clients to leverage the full power of Gemini's advanced AI capabilities.

✨ Features

🧠 Latest Gemini Models

• Gemini 2.5 Pro - Most capable thinking model for complex reasoning
• Gemini 2.5 Flash - Fast thinking model with best price/performance
• Gemini 2.0 Series - Latest generation models with advanced features
• Gemini 1.5 Series - Proven, reliable models for production use

🚀 Advanced Capabilities

• 🧠 Thinking Models - Gemini 2.5 series with step-by-step reasoning
• 🔍 Google Search Grounding - Real-time web information integration
• 📊 JSON Mode - Structured output with schema validation
• 🎯 System Instructions - Behavior customization and control
• 👁️ Vision Support - Image analysis and multimodal capabilities
• 💬 Conversation Memory - Context preservation across interactions

🛠️ Production Ready

• TypeScript - Full type safety and modern development
• Comprehensive Error Handling - Robust error management and recovery
• Rate Limiting - Built-in protection against API abuse
• Detailed Logging - Comprehensive monitoring and debugging
• Input Validation - Secure parameter validation with Zod
• Retry Logic - Automatic retry with exponential backoff

🚀 Quick Start

Prerequisites

• Node.js 16+ (Download)
• Google AI Studio API Key (Get one here)

Installation

Option 1: Global Installation (Recommended)

npm install -g mcp-server-gemini

Option 2: Local Development

git clone https://github.com/gurr-i/mcp-server-gemini-pro.git
cd mcp-server-gemini-pro
npm install
npm run build

Configuration

1. Set up your API key

Option A: Environment Variable

export GEMINI_API_KEY="your_api_key_here"

Option B: .env file

echo "GEMINI_API_KEY=your_api_key_here" > .env

2. Configure Claude Desktop

Add to your claude_desktop_config.json:

For Global Installation:

{
  "mcpServers": {
    "gemini": {
      "command": "mcp-server-gemini",
      "env": {
        "GEMINI_API_KEY": "your_api_key_here"
      }
    }
  }
}

For Local Installation:

{
  "mcpServers": {
    "gemini": {
      "command": "node",
      "args": ["/path/to/mcp-server-gemini-pro/dist/enhanced-stdio-server.js"],
      "env": {
        "GEMINI_API_KEY": "your_api_key_here"
      }
    }
  }
}

3. Restart Claude Desktop

Close and restart Claude Desktop completely for changes to take effect.

💡 Usage Examples

Once configured, you can use Gemini through Claude Desktop with natural language:

Basic Text Generation

"Use Gemini to explain quantum computing in simple terms"
"Generate a creative story about AI using Gemini 2.5 Pro"

Advanced Features

"Use Gemini with JSON mode to extract key points from this text"
"Use Gemini with grounding to get the latest news about AI"
"Generate a Python function using Gemini's thinking capabilities"

Image Analysis

"Analyze this image with Gemini" (attach image)
"What's in this screenshot using Gemini vision?"

Development Tasks

"Use Gemini to review this code and suggest improvements"
"Generate comprehensive tests for this function using Gemini"

⚙️ Configuration

Environment Variables

The server can be configured using environment variables or a .env file:

Required Configuration

# Google AI Studio API Key (required)
GEMINI_API_KEY=your_api_key_here

Optional Configuration

# Logging level (default: info)
# Options: error, warn, info, debug
LOG_LEVEL=info

# Enable performance metrics (default: false)
ENABLE_METRICS=false

# Rate limiting configuration
RATE_LIMIT_ENABLED=true        # Enable/disable rate limiting (default: true)
RATE_LIMIT_REQUESTS=100        # Max requests per window (default: 100)
RATE_LIMIT_WINDOW=60000        # Time window in ms (default: 60000 = 1 minute)

# Request timeout in milliseconds (default: 30000 = 30 seconds)
REQUEST_TIMEOUT=30000

# Environment mode (default: production)
NODE_ENV=production

Environment Setup

Development Environment

# .env for development
GEMINI_API_KEY=your_api_key_here
NODE_ENV=development
LOG_LEVEL=debug
RATE_LIMIT_ENABLED=false
REQUEST_TIMEOUT=60000

Production Environment

# .env for production
GEMINI_API_KEY=your_api_key_here
NODE_ENV=production
LOG_LEVEL=warn
RATE_LIMIT_ENABLED=true
RATE_LIMIT_REQUESTS=100
RATE_LIMIT_WINDOW=60000
REQUEST_TIMEOUT=30000
ENABLE_METRICS=true

Claude Desktop Configuration

Configuration File Locations

OS	Path
macOS	~/Library/Application Support/Claude/claude_desktop_config.json
Windows	%APPDATA%\Claude\claude_desktop_config.json
Linux	~/.config/Claude/claude_desktop_config.json

Basic Configuration

{
  "mcpServers": {
    "gemini": {
      "command": "mcp-server-gemini",
      "env": {
        "GEMINI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Advanced Configuration

{
  "mcpServers": {
    "gemini": {
      "command": "mcp-server-gemini",
      "env": {
        "GEMINI_API_KEY": "your_api_key_here",
        "LOG_LEVEL": "info",
        "RATE_LIMIT_REQUESTS": "200",
        "REQUEST_TIMEOUT": "45000"
      }
    }
  }
}

Local Development Configuration

{
  "mcpServers": {
    "gemini": {
      "command": "node",
      "args": ["/path/to/mcp-server-gemini-pro/dist/enhanced-stdio-server.js"],
      "cwd": "/path/to/mcp-server-gemini-pro",
      "env": {
        "GEMINI_API_KEY": "your_api_key_here",
        "NODE_ENV": "development",
        "LOG_LEVEL": "debug"
      }
    }
  }
}

🛠️ Available Tools

Tool	Description	Key Features
generate_text	Generate text with advanced features	Thinking models, JSON mode, grounding
analyze_image	Analyze images using vision models	Multi-modal understanding, detailed analysis
count_tokens	Count tokens for cost estimation	Accurate token counting for all models
list_models	List all available Gemini models	Real-time model availability and features
embed_text	Generate text embeddings	High-quality vector representations
get_help	Get usage help and documentation	Self-documenting with examples

📊 Model Comparison

Model	Context Window	Features	Best For	Speed
gemini-2.5-pro	2M tokens	Thinking, JSON, Grounding	Complex reasoning, coding	Slower
gemini-2.5-flash ⭐	1M tokens	Thinking, JSON, Grounding	General purpose	Fast
gemini-2.5-flash-lite	1M tokens	Thinking, JSON	High-throughput tasks	Fastest
gemini-2.0-flash	1M tokens	JSON, Grounding	Standard tasks	Fast
gemini-2.0-flash-lite	1M tokens	JSON	Simple tasks	Fastest
gemini-2.0-pro-experimental	2M tokens	JSON, Grounding	Experimental features	Medium
gemini-1.5-pro	2M tokens	JSON	Legacy support	Medium
gemini-1.5-flash	1M tokens	JSON	Legacy support	Fast

🔧 Development

Prerequisites

• Node.js 16+ (Download)
• npm 7+ (comes with Node.js)
• Git for version control
• Google AI Studio API Key (Get one here)

Setup

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

# Install dependencies
npm install

# Set up environment variables
cp .env.example .env
# Edit .env and add your GEMINI_API_KEY

Available Scripts

Development

npm run dev          # Start development server with hot reload
npm run dev:watch    # Start with file watching (nodemon)
npm run build        # Build for production
npm run build:watch  # Build with watch mode
npm run clean        # Clean build directory

Testing

npm test             # Run all tests
npm run test:watch   # Run tests in watch mode
npm run test:coverage # Run tests with coverage report
npm run test:integration # Run integration tests (requires API key)

Code Quality

npm run lint         # Lint TypeScript code
npm run lint:fix     # Fix linting issues automatically
npm run format       # Format code with Prettier
npm run format:check # Check code formatting
npm run type-check   # Run TypeScript type checking
npm run validate     # Run all quality checks (lint + test + type-check)

Release & Distribution

npm run prepack      # Prepare package for publishing
npm run release      # Build, validate, and publish to npm

Project Structure

mcp-server-gemini/
├── src/                          # Source code
│   ├── config/                   # Configuration management
│   │   └── index.ts             # Environment config with Zod validation
│   ├── utils/                   # Utility modules
│   │   ├── logger.ts           # Structured logging system
│   │   ├── errors.ts           # Custom error classes & handling
│   │   ├── validation.ts       # Input validation with Zod
│   │   └── rateLimiter.ts      # Rate limiting implementation
│   ├── enhanced-stdio-server.ts # Main MCP server implementation
│   └── types.ts                # TypeScript type definitions
├── tests/                       # Test suite
│   ├── unit/                   # Unit tests
│   │   ├── config.test.ts      # Configuration tests
│   │   ├── validation.test.ts  # Validation tests
│   │   └── errors.test.ts      # Error handling tests
│   ├── integration/            # Integration tests
│   │   └── gemini-api.test.ts  # Real API integration tests
│   └── setup.ts               # Test setup and utilities
├── docs/                       # Documentation
│   ├── api.md                 # API reference
│   ├── configuration.md       # Configuration guide
│   └── troubleshooting.md     # Troubleshooting guide
├── scripts/                    # Build and utility scripts
│   ├── build.sh              # Production build script
│   ├── dev.sh                # Development script
│   └── test.sh               # Test execution script
├── .github/workflows/         # GitHub Actions CI/CD
│   ├── ci.yml                # Continuous integration
│   └── release.yml           # Automated releases
├── dist/                      # Built output (generated)
├── coverage/                  # Test coverage reports (generated)
└── node_modules/             # Dependencies (generated)

🧪 Testing

Test Suite Overview

The project includes comprehensive testing with unit tests, integration tests, and code coverage reporting.

Running Tests

All Tests

npm test                    # Run all tests (unit tests only by default)
npm run test:watch         # Run tests in watch mode for development
npm run test:coverage      # Run tests with coverage report

Unit Tests

npm test -- --testPathPattern=unit    # Run only unit tests
npm test -- --testNamePattern="config" # Run specific test suites

Integration Tests

Integration tests require a valid GEMINI_API_KEY and make real API calls:

# Set API key and run integration tests
GEMINI_API_KEY=your_api_key_here npm run test:integration

# Or set in .env file and run
npm run test:integration

Test Coverage

npm run test:coverage      # Generate coverage report
open coverage/lcov-report/index.html  # View coverage report (macOS)

Test Structure

Unit Tests (tests/unit/)

• Configuration Tests: Environment variable validation, config loading
• Validation Tests: Input validation, schema validation, sanitization
• Error Handling Tests: Custom error classes, error recovery, retry logic
• Utility Tests: Logger, rate limiter, helper functions

Integration Tests (tests/integration/)

• Gemini API Tests: Real API calls to test connectivity and functionality
• Model Testing: Verify all supported models work correctly
• Feature Testing: JSON mode, grounding, embeddings, token counting

Writing Tests

Test File Structure

// tests/unit/example.test.ts
import { describe, it, expect, beforeEach, afterEach } from '@jest/globals';
import { YourModule } from '../../src/your-module.js';

describe('YourModule', () => {
  beforeEach(() => {
    // Setup before each test
  });

  afterEach(() => {
    // Cleanup after each test
  });

  it('should do something', () => {
    // Test implementation
    expect(result).toBe(expected);
  });
});

Custom Matchers

The test suite includes custom Jest matchers:

expect(response).toBeValidMCPResponse(); // Validates MCP response format

Test Configuration

Tests are configured in jest.config.js with:

• TypeScript Support: Full ES modules and TypeScript compilation
• Coverage Thresholds: Minimum 70% coverage required
• Test Timeout: 30 seconds for integration tests
• Setup Files: Automatic test environment setup

🐳 Docker Deployment

Using Docker

Build and Run

# Build the Docker image
docker build -t mcp-server-gemini .

# Run the container
docker run -d \
  --name mcp-server-gemini \
  -e GEMINI_API_KEY=your_api_key_here \
  -e LOG_LEVEL=info \
  mcp-server-gemini

Using Docker Compose

# Create .env file with your API key
echo "GEMINI_API_KEY=your_api_key_here" > .env

# Start the service
docker-compose up -d

# View logs
docker-compose logs -f

# Stop the service
docker-compose down

Development with Docker

# Start development environment
docker-compose --profile dev up

# This mounts source code for live reloading

Environment-Specific Deployments

Production Deployment

# Production build
docker build --target production -t mcp-server-gemini:prod .

# Run with production settings
docker run -d \
  --name mcp-server-gemini-prod \
  --restart unless-stopped \
  -e GEMINI_API_KEY=your_api_key_here \
  -e NODE_ENV=production \
  -e LOG_LEVEL=warn \
  -e RATE_LIMIT_ENABLED=true \
  -e ENABLE_METRICS=true \
  mcp-server-gemini:prod

Health Checks

# Check container health
docker ps
docker logs mcp-server-gemini

# Manual health check
docker exec mcp-server-gemini node -e "console.log('Health check passed')"

🚀 Deployment Options

1. npm Global Installation

# Install globally
npm install -g mcp-server-gemini

# Run directly
GEMINI_API_KEY=your_key mcp-server-gemini

2. Local Installation

# Clone and build
git clone https://github.com/gurr-i/mcp-server-gemini-pro.git
cd mcp-server-gemini-pro
npm install
npm run build

# Run locally
GEMINI_API_KEY=your_key npm start

3. Docker Deployment

# Using Docker Hub (when published)
docker run -e GEMINI_API_KEY=your_key mcp-server-gemini-pro:latest

# Using local build
docker build -t mcp-server-gemini-pro .
docker run -e GEMINI_API_KEY=your_key mcp-server-gemini-pro

4. Process Manager (PM2)

# Install PM2
npm install -g pm2

# Create ecosystem file
cat > ecosystem.config.js << EOF
module.exports = {
  apps: [{
    name: 'mcp-server-gemini',
    script: './dist/enhanced-stdio-server.js',
    env: {
      NODE_ENV: 'production',
      GEMINI_API_KEY: 'your_api_key_here',
      LOG_LEVEL: 'info'
    }
  }]
}
EOF

# Start with PM2
pm2 start ecosystem.config.js
pm2 save
pm2 startup

🔧 Troubleshooting

Common Issues

1. Server Won't Start

# Check if API key is set
echo $GEMINI_API_KEY

# Verify .env file exists and is readable
cat .env | grep GEMINI_API_KEY

# Check file permissions
ls -la .env
chmod 600 .env

2. API Key Issues

# Test API key manually
curl -H "Content-Type: application/json" \
     -d '{"contents":[{"parts":[{"text":"Hello"}]}]}' \
     -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key=YOUR_API_KEY"

3. Claude Desktop Integration

# Verify config file location (macOS)
ls -la ~/Library/Application\ Support/Claude/claude_desktop_config.json

# Validate JSON syntax
cat claude_desktop_config.json | jq .

# Check server installation
which mcp-server-gemini
npm list -g mcp-server-gemini

4. Rate Limiting

# Temporarily disable rate limiting
export RATE_LIMIT_ENABLED=false

# Increase limits
export RATE_LIMIT_REQUESTS=1000
export RATE_LIMIT_WINDOW=60000

Debug Mode

# Enable debug logging
export LOG_LEVEL=debug
npm run dev

# Or for production
export LOG_LEVEL=debug
npm start

Getting Help

• 🐛 Report Issues
• 💬 Discussions
• 📚 Documentation

🔒 Security

API Key Security

• Never commit API keys to version control
• Use environment variables or secure secret management
• Rotate keys regularly for production use
• Use different keys for development and production

Rate Limiting

• Enable rate limiting in production (RATE_LIMIT_ENABLED=true)
• Configure appropriate limits based on your usage patterns
• Monitor API usage to prevent quota exhaustion

Input Validation

• All inputs are automatically validated and sanitized
• XSS and injection protection built-in
• Schema validation for all tool parameters

Container Security

• Runs as non-root user in Docker
• Read-only filesystem with minimal privileges
• Security scanning in CI/CD pipeline

📚 Documentation

• API Documentation
• Configuration Guide
• Troubleshooting
• Contributing Guide

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Run npm run validate
6. Submit a pull request

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Google AI for the Gemini API
• Anthropic for the Model Context Protocol
• The open-source community for inspiration and feedback

📞 Support

• 🐛 Report Issues
• 💬 Discussions
• 📧 Email Support

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