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AI Peer Review MCP Server

by xyehya
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Python
Hybrid

AI Peer Review MCP Server

Enhance your local LLM responses with real-time peer review from Google Gemini

A Model Context Protocol (MCP) server that enables local language models to request peer review feedback from Google Gemini, dramatically improving response quality through AI collaboration.

🌟 Features

  • Real-time peer review from Google Gemini for any local LLM response

  • Manual trigger system - user controls when to request peer review

  • Detailed feedback analysis - accuracy, completeness, clarity, and improvement suggestions

  • Comprehensive logging - see exactly what feedback Gemini provides

  • Privacy-conscious - only shares content when explicitly requested

  • Free to use - leverages Google Gemini's free tier

  • Easy integration - works with any MCP-compatible local LLM setup

🎯 Use Cases

  • Fact-checking complex or technical responses

  • Quality improvement for educational content

  • Writing enhancement for creative tasks

  • Technical validation for coding explanations

  • Research assistance with multiple AI perspectives

📋 Prerequisites

  • Python 3.8+ installed on your system

  • LMStudio (or another MCP-compatible LLM client)

  • Google AI Studio account (free) for Gemini API access

  • Local LLM with tool calling support (e.g., Llama 3.1, Mistral, Qwen)

🚀 Quick Start

1. Get Google Gemini API Key

  1. Visit Google AI Studio

  2. Sign in with your Google account

  3. Click "Get API key" → "Create API key in new project"

  4. Copy your API key (starts with AIza...)

2. Install the MCP Server

# Clone or create project directory git clone https://github.com/your-repo/ai-peer-review-mcp # Replace with the actual repo URL cd ai-peer-review-mcp # Create a virtual environment (recommended) python3 -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate` # Install dependencies pip install -r requirements.txt # Create environment file cp .env.example .env # Now, edit the .env file and add your API key: # GEMINI_API_KEY=your_actual_api_key_here

3. Review Server Files

requirements.txt:

requests python-dotenv

server.py: (See full code in the repository)

4. Configure LMStudio or any other supported MCP Host (e.g Claude Desktop)

Add this configuration to your LMStudio MCP settings:

{ "mcpServers": { "ai-peer-review": { "command": "python", "args": ["server.py"], "cwd": "/path/to/your/ai-peer-review-mcp", "env": { "GEMINI_API_KEY": "your_actual_api_key_here" } } } }

Finding MCP Settings in LMStudio:

  • Look for: Settings → MCP Servers

  • Or: Tools & Integrations → MCP Configuration

  • Or: Program button → Edit MCP JSON

5. Test the Setup

  1. Restart LMStudio after adding the MCP configuration

  2. Start a new chat in LMStudio

  3. Ask any question: "What is quantum computing?"

  4. Request peer review: "Use the ai_peer_review tool to check and improve your answer"

📚 Usage Examples

Basic Usage

User: What causes climate change? LLM: [Provides initial response about greenhouse gases...] User: Use AI Peer Review to verify and improve that answer LLM: [Calls ai_peer_review tool, receives feedback, provides enhanced response]

Technical Questions

User: Explain how neural networks work LLM: [Initial technical explanation...] User: Can you use ai_peer_review to make sure the explanation is accurate? LLM: [Enhanced response with better technical details and examples]

Creative Tasks

User: Write a short story about AI LLM: [Initial creative writing...] User: Use peer review to improve the story structure and clarity LLM: [Improved story with better narrative flow and character development]

🔧 Configuration Options

Environment Variables

  • GEMINI_API_KEY - Your Google Gemini API key (required)

Customization

You can modify the peer review prompt in server.py to focus on specific aspects:

review_prompt = f"""PEER REVIEW REQUEST: # Customize this section for your specific needs # Examples: # - Focus on technical accuracy for coding questions # - Emphasize creativity for writing tasks # - Prioritize safety for medical/legal topics ... """

📊 Monitoring and Logs

The server creates detailed logs in mcp-server.log:

# Watch logs in real-time tail -f mcp-server.log # View recent activity cat mcp-server.log | tail -50

Log Information Includes:

  • Tool calls from LMStudio

  • Requests sent to Gemini

  • Raw Gemini responses

  • Parsed feedback

  • Error details

🐛 Troubleshooting

Common Issues

"Tool not available"

  • Verify MCP server configuration in LMStudio

  • Ensure your local model supports tool calling

  • Restart LMStudio after configuration changes

"GEMINI_API_KEY not found"

  • Check your .env file exists and has the correct key

  • Verify API key is valid in Google AI Studio

  • Ensure environment variable is properly set in LMStudio config

"Rate limit exceeded"

  • Google Gemini free tier has generous limits

  • Wait a moment and try again

  • Check Google AI Studio quota usage

"Model not found"

  • API model names change over time

  • Update GEMINI_API_URL in server.js if needed

  • Check Google's latest API documentation

Debug Mode

Run the server manually to see detailed output. Make sure your virtual environment is active.

export GEMINI_API_KEY=your_api_key_here python server.py

🔒 Privacy and Security

  • Data sharing only on request - content is only sent to Gemini when explicitly triggered

  • No persistent storage - conversations are not stored or logged beyond current session

  • API key security - keep your Gemini API key private and secure

  • Local processing - MCP runs entirely on your machine

🚧 Limitations

  • Requires tool-calling models - basic instruction-following models won't work

  • Internet connection required - needs access to Google Gemini API

  • Rate limits - subject to Google Gemini API quotas (free tier is generous)

  • Language support - optimized for English, other languages may work but aren't tested

🛣️ Roadmap

  • Multi-provider support - Add Groq, DeepSeek, and other AI APIs

  • Smart routing - Automatic provider selection based on question type

  • Confidence thresholds - Auto-trigger peer review for uncertain responses

  • Custom review templates - Domain-specific review criteria

  • Usage analytics - Track improvement metrics and API usage

  • Batch processing - Review multiple responses at once

🤝 Contributing

We welcome contributions! Here's how to help:

Development Setup

git clone https://github.com/your-repo/ai-peer-review-mcp # Replace with your repo URL cd ai-peer-review-mcp # Create and activate virtual environment python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies pip install -r requirements.txt # Set up your environment cp .env.example .env # --> Add your GEMINI_API_KEY to the .env file echo "Development environment ready. Run with 'python server.py'"

Ways to Contribute

  • 🐛 Bug reports - Open issues for any problems you encounter

  • 💡 Feature requests - Suggest new capabilities or improvements

  • 📖 Documentation - Improve setup guides, add examples

  • 🔧 Code contributions - Submit pull requests for fixes or features

  • 🧪 Testing - Try with different models and report compatibility

  • 🌍 Localization - Help support more languages

Contribution Guidelines

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/amazing-feature)

  3. Make your changes with clear, descriptive commits

  4. Add tests if applicable

  5. Update documentation for any new features

  6. Submit a pull request with a clear description

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • Anthropic - For creating the Model Context Protocol standard

  • Google - For providing the Gemini API

  • LMStudio - For excellent MCP integration

  • Community contributors - Everyone who helps improve this project

📞 Support

🌟 Star History

If this project helps you, please consider giving it a star on GitHub! ⭐

Made with ❤️ for the AI community

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