PMCP - Perfect Model Context Protocol Server

PMCP - Perfect Model Context Protocol Server

A comprehensive, production-ready Model Context Protocol (MCP) server with advanced AI capabilities, web-based administration, and extensive plugin ecosystem support.

🚀 Features

Core Capabilities

• 🤖 AI Integration: Support for OpenAI, Anthropic, and Google Gemini models
• 🔌 MCP Plugin Hub: Discover, install, and manage MCP plugins with ease
• 💾 Multi-Database Support: Redis for caching, MongoDB for persistence
• 🌐 Web Admin Interface: Comprehensive dashboard for server management
• 🔒 SSH/SFTP Access: Secure file operations and remote access
• 📊 Real-time Monitoring: Live metrics, logs, and system status

Advanced Services

• 🧠 Memory Management: Persistent context and session handling
• 📚 RAG (Retrieval-Augmented Generation): Document indexing and semantic search
• 🔍 Code Analysis: AI-powered code review and improvement suggestions
• 🎭 Playwright Integration: Web automation and browser control
• 🔗 Sequential Thinking: Chain-of-thought reasoning capabilities

Administration & Monitoring

• 📈 Dashboard: Real-time system metrics and performance monitoring
• 🗂️ File Management: Web-based file browser and editor
• 👥 User Management: Authentication and access control
• 🔧 Configuration: Dynamic settings management
• 📋 Logging: Comprehensive structured logging with multiple outputs

🛠️ Quick Start

Prerequisites

• Python 3.8 or higher
• Redis server
• MongoDB server
• Git

Installation

1. Clone the repository
   git clone https://github.com/NoobyNull/PMCP.git cd PMCP
2. Set up Python environment
   python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt
3. Configure databases
   # Install and start Redis sudo apt-get install redis-server sudo systemctl start redis-server # Install and start MongoDB sudo apt-get install mongodb sudo systemctl start mongodb
4. Configure the server
   cp config/server.yaml.example config/server.yaml # Edit config/server.yaml with your settings
5. Start the server
   # Development mode python admin_server.py # Production mode (systemd service) sudo ./setup_service.sh

Access Points

• Admin Interface: http://localhost:8080
• API Server: http://localhost:8000
• SSH/SFTP: localhost:2222

📖 Documentation

Quick Setup Guides

• 🚀 Deployment Guide - Production deployment instructions
• 🔧 Admin Interface Guide - Web interface overview
• 🔌 MCP Hub Guide - Plugin management
• 🤖 Augment Integration - AI assistant setup

Advanced Configuration

• 🌐 LAN Access Setup - Network configuration
• 🔐 Service Management - Systemd service setup
• 📊 Logging Configuration - Log management
• 🎨 UI/UX Customization - Interface theming

🔌 MCP Plugin Ecosystem

PMCP includes a built-in plugin hub that provides access to the Model Context Protocol ecosystem:

Featured Plugins

• Filesystem MCP Server - Secure file system operations
• GitHub MCP Server - Repository management and operations
• Brave Search MCP Server - Web search capabilities
• SQLite/PostgreSQL MCP Servers - Database operations
• And many more...

Plugin Management

• Browse and search available plugins
• One-click installation with progress tracking
• Automatic dependency management
• Plugin status monitoring and updates

🏗️ Architecture

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and add tests
4. Run the test suite: pytest
5. Submit a pull request

📄 License

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

🙏 Acknowledgments

• Model Context Protocol - The open standard this server implements
• FastAPI - The web framework powering our APIs
• Bootstrap - UI framework for the admin interface
• The MCP community for their excellent plugins and contributions

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Project Wiki

PMCP - Empowering AI assistants with comprehensive context and capabilities 🚀