MCP Server Gemini
# ๐ค 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.
## โจ Features
### ๐ง **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
### ๐ **Advanced Capabilities**
- **๐ง Thinking Models** - Gemini 2.5 series with step-by-step reasoning
- **๐ Google Search Grounding** - Real-time web information integration
- **๐ JSON Mode** - Structured output with schema validation
- **๐ฏ System Instructions** - Behavior customization and control
- **๐๏ธ Vision Support** - Image analysis and multimodal capabilities
- **๐ฌ Conversation Memory** - Context preservation across interactions
### ๐ ๏ธ **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
## ๐ Quick Start
### Prerequisites
- **Node.js 16+** ([Download](https://nodejs.org/))
- **Google AI Studio API Key** ([Get one here](https://aistudio.google.com/app/apikey))
### Installation
#### Option 1: Global Installation (Recommended)
```bash
npm install -g mcp-server-gemini
```
#### Option 2: Local Development
```bash
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**
```bash
export GEMINI_API_KEY="your_api_key_here"
```
**Option B: .env file**
```bash
echo "GEMINI_API_KEY=your_api_key_here" > .env
```
#### 2. Configure Claude Desktop
Add to your `claude_desktop_config.json`:
**For Global Installation:**
```json
{
"mcpServers": {
"gemini": {
"command": "mcp-server-gemini",
"env": {
"GEMINI_API_KEY": "your_api_key_here"
}
}
}
}
```
**For Local Installation:**
```json
{
"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.
## ๐ก 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"
```
## โ๏ธ Configuration
### Environment Variables
The server can be configured using environment variables or a `.env` file:
#### Required Configuration
```bash
# Google AI Studio API Key (required)
GEMINI_API_KEY=your_api_key_here
```
#### Optional Configuration
```bash
# 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
```bash
# .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
```bash
# .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
```json
{
"mcpServers": {
"gemini": {
"command": "mcp-server-gemini",
"env": {
"GEMINI_API_KEY": "your_api_key_here"
}
}
}
}
```
#### Advanced Configuration
```json
{
"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
```json
{
"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"
}
}
}
}
```
## ๐ ๏ธ 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 |
## ๐ 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** โญ | 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 |
## ๐ง Development
### Prerequisites
- **Node.js 16+** ([Download](https://nodejs.org/))
- **npm 7+** (comes with Node.js)
- **Git** for version control
- **Google AI Studio API Key** ([Get one here](https://aistudio.google.com/app/apikey))
### Setup
```bash
# 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
```bash
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
```bash
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
```bash
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
```bash
npm run prepack # Prepare package for publishing
npm run release # Build, validate, and publish to npm
```
### Project Structure
```
mcp-server-gemini/
โโโ src/ # Source code
โ โโโ config/ # Configuration management
โ โ โโโ index.ts # Environment config with Zod validation
โ โโโ utils/ # Utility modules
โ โ โโโ logger.ts # Structured logging system
โ โ โโโ errors.ts # Custom error classes & handling
โ โ โโโ validation.ts # Input validation with Zod
โ โ โโโ rateLimiter.ts # Rate limiting implementation
โ โโโ enhanced-stdio-server.ts # Main MCP server implementation
โ โโโ types.ts # TypeScript type definitions
โโโ tests/ # Test suite
โ โโโ unit/ # Unit tests
โ โ โโโ config.test.ts # Configuration tests
โ โ โโโ validation.test.ts # Validation tests
โ โ โโโ errors.test.ts # Error handling tests
โ โโโ integration/ # Integration tests
โ โ โโโ gemini-api.test.ts # Real API integration tests
โ โโโ setup.ts # Test setup and utilities
โโโ docs/ # Documentation
โ โโโ api.md # API reference
โ โโโ configuration.md # Configuration guide
โ โโโ troubleshooting.md # Troubleshooting guide
โโโ scripts/ # Build and utility scripts
โ โโโ build.sh # Production build script
โ โโโ dev.sh # Development script
โ โโโ test.sh # Test execution script
โโโ .github/workflows/ # GitHub Actions CI/CD
โ โโโ ci.yml # Continuous integration
โ โโโ release.yml # Automated releases
โโโ dist/ # Built output (generated)
โโโ coverage/ # Test coverage reports (generated)
โโโ node_modules/ # Dependencies (generated)
```
## ๐งช Testing
### Test Suite Overview
The project includes comprehensive testing with unit tests, integration tests, and code coverage reporting.
### Running Tests
#### All Tests
```bash
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
```bash
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:
```bash
# 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
```bash
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
```typescript
// 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:
```typescript
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
## ๐ณ Docker Deployment
### Using Docker
#### Build and Run
```bash
# 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
```bash
# 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
```bash
# Start development environment
docker-compose --profile dev up
# This mounts source code for live reloading
```
### Environment-Specific Deployments
#### Production Deployment
```bash
# 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
```bash
# 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')"
```
## ๐ Deployment Options
### 1. npm Global Installation
```bash
# Install globally
npm install -g mcp-server-gemini
# Run directly
GEMINI_API_KEY=your_key mcp-server-gemini
```
### 2. Local Installation
```bash
# 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
```bash
# 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)
```bash
# 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
```
## ๐ง Troubleshooting
### Common Issues
#### 1. Server Won't Start
```bash
# 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
```bash
# 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
```bash
# 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
```bash
# Temporarily disable rate limiting
export RATE_LIMIT_ENABLED=false
# Increase limits
export RATE_LIMIT_REQUESTS=1000
export RATE_LIMIT_WINDOW=60000
```
### Debug Mode
```bash
# Enable debug logging
export LOG_LEVEL=debug
npm run dev
# Or for production
export LOG_LEVEL=debug
npm start
```
### Getting Help
- ๐ [Report Issues](https://github.com/gurr-i/mcp-server-gemini-pro/issues)
- ๐ฌ [Discussions](https://github.com/gurr-i/mcp-server-gemini-pro/discussions)
- ๐ [Documentation](docs/)
## ๐ 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
## ๐ Documentation
- [API Documentation](docs/api.md)
- [Configuration Guide](docs/configuration.md)
- [Troubleshooting](docs/troubleshooting.md)
- [Contributing Guide](CONTRIBUTING.md)
## ๐ค Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) 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
## ๐ License
MIT License - see [LICENSE](LICENSE) file for details.
## ๐ Acknowledgments
- Google AI for the Gemini API
- Anthropic for the Model Context Protocol
- The open-source community for inspiration and feedback
## ๐ Support
- ๐ [Report Issues](https://github.com/gurr-i/mcp-server-gemini-pro/issues)
- ๐ฌ [Discussions](https://github.com/gurr-i/mcp-server-gemini-pro/discussions)
- ๐ง [Email Support](mailto:gopal@ph.iitr.ac.in)
---
<div align="center">
<strong>Made with โค๏ธ By Gurveer for the AI development community</strong>
</div>
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose with no overlap: image analysis, token counting, text embedding, text generation, help retrieval, and model listing. The descriptions reinforce these distinct functions, making tool selection unambiguous for an agent.
Most tools follow a consistent verb_noun pattern (analyze_image, count_tokens, embed_text, generate_text, list_models), but get_help deviates slightly with a 'get_' prefix. The naming is still highly readable and predictable overall.
With 6 tools, this server is well-scoped for Gemini AI interactions. Each tool earns its place by covering distinct aspects: vision, token management, embeddings, text generation, help, and model discovery, without being overly sparse or bloated.
The toolset covers core Gemini AI workflows comprehensively, including text and image processing, embeddings, and model management. A minor gap exists in lacking explicit tools for chat/multi-turn conversations or file uploads, but agents can work around this using generate_text and analyze_image.