Enables access to Google's Gemini AI models for code analysis, security reviews, and performance suggestions with support for massive context windows (1M+ tokens)
Claude Gemini MCP Integration
🚀 What's Coming Next: I'm building an AI Agent Feedback Loop System that enables intelligent collaboration between Claude Code and Gemini AI. This will create a continuous improvement cycle where both AI agents learn from each other's suggestions, creating smarter code analysis and more contextual development assistance. Starting with Claude Code, then expanding to other IDEs. Stay tuned!
A lightweight integration that brings Google's Gemini AI capabilities to Claude Code through MCP (Model Context Protocol)
This project connects Claude Code (your coding assistant) with Google's Gemini AI models. Think of it as adding a second AI expert to your development team - one that can read and understand massive amounts of code at once (1M+ tokens, which is like reading hundreds of code files simultaneously).
With this integration, you can ask Gemini questions about your code, get security reviews, performance suggestions, and architectural advice - all without leaving your coding environment. It automatically chooses the right AI model for each task: fast responses for quick questions, deeper analysis for complex problems.
Table of Contents
- Key Features
- How It Works
- Architecture Overview
- Quick Start
- Usage Examples
- Benefits
- Usage Examples with Slash Commands
- What's Next?
- Need Help?
- Further Documentation
- Changelog
- Contributing
- License
- Credits
Key Features
- Quick Query - Ask Gemini any development question instantly
- Code Analysis - Deep analysis with security, performance, and architecture insights
- Codebase Analysis - Full project analysis using Gemini's massive context window
- Automated Hooks - Pre-edit analysis, pre-commit review, and session summaries
- 20+ Slash Commands - Simple shortcuts like
/g
,/analyze
,/security
- Smart Model Selection - Flash for speed, Pro for depth, automatic fallback
- Real-time Streaming - Live output with progress indicators
How It Works
Architecture Overview
The Gemini MCP server uses a shared architecture where one installation serves multiple AI clients and projects:
Key Benefits
- One installation serves all AI clients and projects
- No project pollution - keeps your projects clean
- Easy maintenance - update once, benefits everywhere
- Smart fallbacks - API-first approach with CLI backup
File Structure
Project Structure (with hooks enabled)
Multi-Client Support
The shared MCP architecture supports multiple AI clients simultaneously:
Supported Clients:
- Claude Desktop - Core MCP tools only
- Claude Code - Core MCP tools + hooks (if configured)
- VS Code with Claude Code extension - Core MCP tools + hooks (if configured)
- Cursor IDE - Core MCP tools only
- Windsurf - Core MCP tools only
- VS Code with other MCP extensions - Core MCP tools only
- Any MCP-compatible client - Core MCP tools only
Important: Hook functionality (.claude/hooks.json) is exclusive to Claude Code ecosystem (Claude Code standalone + VS Code with Claude Code extension). No other AI client currently supports this automation system.
Quick Start
New to this project? Here's what you need to do:
- Get your Google API key from Google AI Studio
- Follow the complete setup guide in docs/SETUP/SETUP.md
- Test the integration with a simple query
- Explore the 20+ slash commands in docs/README-SLASH-COMMANDS.md
Installation time: ~5 minutes | Prerequisites: Python 3.10+, Node.js 16+
Usage Examples
Quick Development Questions
Code Analysis
Full Project Analysis
Automated Hooks System
Benefits
For Developers
- Instant access to Gemini's advanced AI capabilities
- Seamless integration within Claude Code workflow
- Smart model selection - fast responses when speed matters, deep analysis when needed
- Real-time feedback during long analysis tasks
For Code Quality
- Security analysis using Gemini's latest training data
- Performance insights from large-scale pattern recognition
- Architecture guidance based on current best practices
- Error prevention through pre-edit analysis
For Productivity
- Reduced context switching - stay in Claude Code
- Faster debugging with AI-powered error analysis
- Better decisions through comprehensive code review
- Learning acceleration via instant expert guidance
Usage Examples with Slash Commands
The slash commands provide a much simpler way to access Gemini's powerful analysis without remembering the full MCP tool syntax:
See docs/README-SLASH-COMMANDS.md for all available shortcuts.
What's Next?
Once everything is working:
- Try the tools - Start with simple queries:
/mcp__gemini-mcp__gemini_quick_query "How do I optimize React performance?"
- Analyze some code - Analyze specific functions:
/mcp__gemini-mcp__gemini_analyze_code "your function code here" security
- Review your project - Get architectural insights:
/mcp__gemini-mcp__gemini_codebase_analysis "./src" architecture
- Explore slash commands - Check
docs/README-SLASH-COMMANDS.md
for 20+ shortcuts
Need Help?
- Submit an Issue: If you encounter any problems, please submit an issue on our GitHub repository with details about your environment, steps to reproduce, and any error messages you received.
- Use Labels: When submitting issues, please use appropriate labels/tags such as
bug
,feature-request
,documentation
, and so on (available labels) to help us categorize and address your concerns more efficiently. - Test files: Check the
tests/
folder for examples and testing scripts - Slash commands: See
docs/README-SLASH-COMMANDS.md
for comprehensive command reference - Console Logs: Check your Claude Desktop/Code console for detailed error messages that can help diagnose issues
Further Documentation
- docs/SETUP/SETUP.md - Complete installation and configuration guide
- docs/README-SLASH-COMMANDS.md - Slash commands reference
- docs/SECURITY.md - Security documentation and hardening details
- docs/TESTING.md - Testing guide and best practices
Changelog
📋 Complete Changelog: For detailed release notes and full version history, see docs/CHANGELOG.md
Contributing
This project is designed to be lightweight and focused. The core functionality is complete, but contributions are welcome for:
- Additional analysis types
- Better error handling
- Performance optimizations
- Documentation improvements
How to Contribute
- Fork the repository
- Create a feature branch:
git checkout -b feature/your-feature-name
- Make your changes: Implement your feature or bug fix
- Test with MCP integration: Ensure your changes work with this repo's MCP server
- Submit a pull request: Push to your fork and submit a PR to the main repository
Development Guidelines
- Clean Python Code: Use type hints, docstrings, and follow the existing code structure
- Security-First Approach: Implement proper input sanitization and API key protection
- Modular Design: Keep functions focused and reusable with clear error handling
- MCP Protocol Compliance: Follow the MCP server specifications for all tools
- Comprehensive Documentation: Document all tools with clear descriptions and schemas
- Concise Code Comments: Add brief comments to explain code blocks' purpose and functionality
- Fallback Mechanisms: Implement API with CLI fallbacks for resilience
- Testing: Verify changes work with both direct API and CLI integrations
Development Setup
To set up a local development environment for this MCP server:
Quick Setup (Recommended)
The setup script will:
- Create a Python virtual environment (
.venv
) - Install all production and development dependencies
- Set up unified Husky hooks for code quality and commit validation
- Run a quick test to verify everything is working
Manual Setup
If you prefer to set up manually:
Development Workflow
Once set up, your typical development workflow will be:
Important Notes:
- The virtual environment (
.venv
) is automatically ignored by git - Husky hooks will run automatically on every commit to ensure code quality and enforce conventional commit messages
- If you're using an IDE like Windsurf, make sure it's configured to use the virtual environment
License
MIT License
Copyright (c) 2025 Dr Muhammad Aizat Hawari
This project is licensed under the MIT License - see the LICENSE.md file for details.
Credits
Special thanks to the following tools and platforms that assisted during the research and development of this MCP:
- Claude - AI assistant for code development and documentation
- Perplexity - AI-powered research and information gathering
- Warp Terminal - Modern terminal for enhanced development workflow
This server cannot be installed
A lightweight server that connects Claude Code with Google's Gemini AI models, allowing developers to leverage Gemini's massive context window (1M+ tokens) for code analysis without leaving their coding environment.
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