giteamcp/README.md

MCP Server - AI-Powered Code Editor

A comprehensive server that automatically clones Gitea repositories, analyzes code with AI models (Gemini/OpenAI), applies intelligent code changes, and commits them back to the repository.

🚀 Features

• Repository Management: Clone repositories from Gitea with authentication
• AI-Powered Analysis: Use Gemini CLI or OpenAI to analyze and edit code
• Model Selection: Choose specific AI models (e.g., gemini-1.5-pro, gpt-4)
• Real-time Progress Tracking: Web interface with live status updates
• Modern UI: Beautiful, responsive frontend with progress indicators
• Background Processing: Asynchronous task processing with status monitoring
• Comprehensive Logging: Full logging to both console and file
• Docker Support: Easy deployment with Docker and docker-compose

📋 Prerequisites

• Python 3.8+
• Git
• API keys for AI models (Gemini or OpenAI)

🛠️ Installation

Option 1: Docker (Recommended)

1. Clone the repository

   git clone <your-repo-url>
   cd mcp-server
   

2. Build and run with Docker Compose

   docker-compose up --build
   

3. Or build and run manually

   docker build -t mcp-server .
   docker run -p 8000:8000 mcp-server
   

Option 2: Local Installation

1. Clone the repository

   git clone <your-repo-url>
   cd mcp-server
   

2. Install Python dependencies

   pip install -r requirements.txt
   

3. Install Gemini CLI (if using Gemini)

   # Download from GitHub releases
   curl -L https://github.com/google/generative-ai-go/releases/latest/download/gemini-linux-amd64 -o /usr/local/bin/gemini
   chmod +x /usr/local/bin/gemini
   

4. Start the server

   python main.py
   # or
   python start.py
   

🚀 Usage

Using the Web Interface

1. Open your browser and navigate to http://localhost:8000

2. Fill in the repository details:

   • Gitea Repository URL: Your repository URL (e.g., http://157.66.191.31:3000/user/repo.git)
   • Gitea Token: Your Gitea access token (get from Settings → Applications → Generate new token)
   • AI Model: Choose between Gemini CLI or OpenAI
   • Model Name: Specify the exact model (e.g., gemini-1.5-pro, gpt-4)
   • API Key: Your AI model API key
   • Prompt: Describe what changes you want to make to the code

3. Click "Process Repository" and monitor the progress

API Endpoints

• GET / - Web interface
• POST /process - Start repository processing
• GET /status/{task_id} - Get processing status
• GET /health - Health check

🔧 Configuration

Environment Variables

Variable	Description	Default
HOST	Server host	0.0.0.0
PORT	Server port	8000

Supported AI Models

Gemini Models:

• gemini-1.5-pro (recommended)
• gemini-1.5-flash
• gemini-1.0-pro

OpenAI Models:

• gpt-4
• gpt-4-turbo
• gpt-3.5-turbo

Supported File Types

The system analyzes and can modify:

• Python (.py)
• JavaScript (.js, .jsx)
• TypeScript (.ts, .tsx)
• HTML (.html)
• CSS (.css)
• JSON (.json)
• Markdown (.md)

📁 Project Structure

mcp-server/
├── main.py              # FastAPI application
├── requirements.txt     # Python dependencies
├── Dockerfile          # Docker configuration
├── docker-compose.yml  # Docker Compose configuration
├── README.md           # This file
├── templates/
│   └── index.html      # Frontend template
├── static/
│   ├── style.css       # Frontend styles
│   └── script.js       # Frontend JavaScript
└── logs/               # Log files (created by Docker)

🔄 How It Works

1. Repository Cloning: Authenticates with Gitea and clones the repository
2. AI Analysis: Sends code and prompt to selected AI model
3. Code Modification: Applies AI-suggested changes to the codebase
4. Commit & Push: Commits changes and pushes back to Gitea

🎯 Example Prompts

• "Add error handling to all API endpoints"
• "Optimize database queries for better performance"
• "Add comprehensive logging throughout the application"
• "Refactor the authentication system to use JWT tokens"
• "Add unit tests for all utility functions"

📊 Logging

The server provides comprehensive logging:

• Console Output: Real-time logs in the terminal
• File Logging: Logs saved to mcp_server.log
• Task-specific Logging: Each task has detailed logging with task ID

Viewing Logs

Docker:

# View container logs
docker logs <container_id>

# Follow logs in real-time
docker logs -f <container_id>

Local:

# View log file
tail -f mcp_server.log

🔒 Security Considerations

• API keys are sent from frontend and not stored
• Use HTTPS in production
• Implement proper authentication for the web interface
• Regularly update dependencies
• Monitor API usage and costs

🐛 Troubleshooting

Common Issues

1. Repository cloning fails

   • Verify Gitea token is valid and has repository access
   • Check repository URL format
   • Ensure repository exists and is accessible
   • Make sure token has appropriate permissions (read/write)

2. AI model errors

   • Verify API keys are correct
   • Check model name spelling
   • Ensure internet connectivity

3. Gemini CLI not found

   • Install Gemini CLI: curl -L https://github.com/google/generative-ai-go/releases/latest/download/gemini-linux-amd64 -o /usr/local/bin/gemini && chmod +x /usr/local/bin/gemini

Logs

Check the logs for detailed error messages and processing status:

• Frontend: Real-time logs in the web interface
• Backend: Console and file logs with detailed information

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

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

🆘 Support

For issues and questions:

1. Check the troubleshooting section
2. Review the logs in the web interface and console
3. Create an issue in the repository

---

Note: This tool modifies code automatically. Always review changes before deploying to production environments.