MCP AI POC

# MCP AI POC (Still in progress) [](https://github.com/drewtech/mcp-ai-poc/actions) **MCP (Model Context Protocol) Server** with AI-powered development tools and resources. ## What This Project Provides This project provides a comprehensive **MCP server** that offers: ### 🛠️ **AI-Powered Tools** - **Code Generation**: Generate production-ready code from specifications - **Code Refactoring**: Improve existing code for better maintainability, performance, or readability - **Debugging Assistant**: Analyze and fix code issues with detailed explanations - **Performance Optimization**: Identify bottlenecks and optimize code performance - **Test Generation**: Create comprehensive unit tests for any codebase ### 📋 **Smart Prompts** - **Code Analysis**: Deep analysis for quality, security, and best practices - **Documentation Generation**: Auto-generate docs in multiple styles (Google, Sphinx, NumPy) - **Code Review**: Comprehensive reviews with focus on specific areas - **Concept Explanation**: Explain programming concepts at different skill levels ### 📚 **Knowledge Resources** - **Python Coding Guidelines**: Best practices and style guides - **Design Patterns Reference**: Common patterns with examples - **Security Best Practices**: Security guidelines and vulnerability prevention - **Performance Optimization Guide**: Strategies for faster, more efficient code ## Quick Start ### 1. Installation ```bash # Clone and set up the project git clone <your-repo-url> cd mcp-ai-poc # 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 pip install -r dev-requirements.txt # For development and testing # Install in editable mode pip install -e . ``` ### 2. Set Up Environment ```bash # Set your OpenAI API key export OPENAI_API_KEY="your-api-key-here" ``` ### 3. Run as MCP Server ```bash # Start MCP server python src/run.py # or python -m mcp_poc.standalone_server ``` ### 4. Run Tests (Optional) ```bash # Run all tests pytest # Run with verbose output pytest -v # Run specific test file pytest src/tests/test_server.py ``` ## MCP Integration ### Using with MCP-Compatible Clients This server implements the Model Context Protocol and can be used with any MCP-compatible client like Claude Desktop, etc. #### Configuration Example Add to your MCP client configuration: ```json { "mcpServers": { "mcp-ai-poc": { "command": "python", "args": ["/path/to/mcp-ai-poc/src/run.py"], "env": { "OPENAI_API_KEY": "your-api-key-here" } } } } ``` #### Available MCP Capabilities **Tools:** - `generate_code` - Generate code from specifications - `refactor_code` - Refactor existing code - `debug_code` - Debug and fix code issues - `optimize_performance` - Optimize code performance - `generate_tests` - Generate unit tests **Prompts:** - `analyze_code` - Comprehensive code analysis - `generate_documentation` - Create documentation - `code_review` - Perform code reviews - `explain_concept` - Explain programming concepts **Resources:** - `coding-guidelines://python` - Python best practices - `patterns://design-patterns` - Design patterns reference - `security://best-practices` - Security guidelines - `performance://optimization-guide` - Performance tips ## Key Features ### 🚀 **Comprehensive MCP Server** This project provides a full-featured MCP server with production-ready capabilities: ### 🔧 **Architecture** - **Standalone Server**: No external MCP dependencies required - **JSON-RPC Protocol**: Implements MCP's communication protocol - **Modular Design**: Separate modules for AI tools, server logic, and utilities - **Error Handling**: Robust error handling for production use - **Comprehensive Testing**: Full test suite with pytest for reliability ### 💡 **Practical AI Tools** Each tool is designed to solve real development problems: - **Code Generation**: Handles specifications with context awareness - **Refactoring**: Focuses on specific goals (performance, readability, etc.) - **Debugging**: Provides root cause analysis and fixes - **Optimization**: Identifies bottlenecks with trade-off analysis - **Testing**: Generates comprehensive test suites ### 📖 **Rich Knowledge Base** Built-in resources provide instant access to: - Coding standards and best practices - Security guidelines - Performance optimization strategies - Design pattern references ## Use Cases ### For Individual Developers - **Code Review**: Get instant feedback on your code - **Learning**: Understand concepts and best practices - **Debugging**: Get help with tricky bugs - **Documentation**: Generate docs automatically ### For Teams - **Consistency**: Enforce coding standards across the team - **Knowledge Sharing**: Built-in best practices and patterns - **Code Quality**: Automated analysis and suggestions - **Onboarding**: Help new team members learn patterns ### For AI Assistants - **Enhanced Capabilities**: Provide AI assistants with powerful development tools - **Context-Aware Help**: Tools understand programming context - **Structured Responses**: Well-formatted, actionable output - **Resource Access**: Built-in knowledge base for common questions ## Project Structure ``` src/ ├── mcp_poc/ # Main package │ ├── __init__.py # Package initialization │ ├── app.py # Main application (chat + server entry) │ ├── ai_tools.py # OpenAI client and utilities │ ├── standalone_server.py # MCP server implementation │ └── mcp_server.py # Alternative MCP server (requires mcp package) ├── tests/ # Test suite │ ├── test_app.py # Application tests │ └── test_server.py # MCP server tests └── run.py # Main entry point Configuration & Dependencies: ├── requirements.txt # Runtime dependencies ├── dev-requirements.txt # Development and testing dependencies ├── pyproject.toml # Project configuration and build settings └── mcp_config.json # MCP client configuration example Documentation: # Comprehensive docs ├── docs/ │ ├── CONTEXT.md # Project overview │ ├── ARCHITECTURE.md # Technical details │ ├── API.md # API reference │ ├── DEVELOPMENT.md # Development guide │ ├── EXAMPLES.md # Usage examples │ └── TROUBLESHOOTING.md # Common issues └── README.md # This file ``` ## Documentation ### For AI Assistants - **[📋 Project Context](docs/CONTEXT.md)** - High-level overview and AI guidelines - **[🏗️ Architecture](docs/ARCHITECTURE.md)** - Code structure and design patterns - **[📚 API Reference](docs/API.md)** - Detailed function and class documentation ### For Developers - **[🛠️ Development Guide](docs/DEVELOPMENT.md)** - Setup, testing, and contribution guidelines - **[💡 Examples](docs/EXAMPLES.md)** - Usage examples and integration patterns - **[🐛 Troubleshooting](docs/TROUBLESHOOTING.md)** - Common issues and solutions ## Enhanced Features ### 🎯 **Intelligent Code Analysis** - Multi-dimensional code quality assessment - Security vulnerability detection - Performance bottleneck identification - Best practice recommendations ### 🔄 **Context-Aware Refactoring** - Goal-specific refactoring (performance, readability, maintainability) - Language-specific optimizations - Preservation of functionality - Clear change explanations ### 🐛 **Advanced Debugging** - Root cause analysis - Step-by-step problem breakdown - Fixed code with explanations - Prevention strategies ### ⚡ **Performance Optimization** - Algorithmic improvements - Memory usage optimization - Concurrency recommendations - Trade-off analysis ### 🧪 **Comprehensive Testing** - Framework-specific test generation - Edge case coverage - Multiple testing strategies - Production-ready test code ## Next Steps for Further Enhancement ### 1. **Add More Tools** - **API Documentation Generator**: Auto-generate API docs - **Database Query Optimizer**: Optimize SQL queries - **Dependency Analyzer**: Analyze and update dependencies - **Code Complexity Analyzer**: Measure and reduce complexity ### 2. **Enhanced Resources** - **Framework-Specific Guides**: React, Django, FastAPI guides - **Language References**: Support for more programming languages - **Architecture Patterns**: Microservices, event-driven, etc. - **DevOps Best Practices**: CI/CD, deployment, monitoring ### 3. **Integration Features** - **Git Integration**: Analyze commits, generate changelogs - **IDE Plugins**: VS Code, IntelliJ extensions - **CI/CD Integration**: Automated code analysis in pipelines - **Slack/Teams Bots**: Team collaboration features ### 4. **Advanced AI Features** - **Multi-Model Support**: Support for different AI models - **Custom Training**: Fine-tune models for specific codebases - **Code Similarity Detection**: Find similar code patterns - **Automated Testing**: AI-generated integration tests This enhanced MCP server transforms your simple chat client into a powerful development assistant that can be integrated into any MCP-compatible environment, providing immediate value to developers and AI assistants alike.