PMCP - Perfect Model Context Protocol Server

PMCP - Perfect Model Context Protocol Server

A comprehensive production-ready MCP server with AI integration, plugin management, and web-based administration. Features multi-database support, RAG capabilities, SSH/SFTP access, and a built-in plugin hub for managing the MCP ecosystem.

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PMCP - Perfect Model Context Protocol Server

License: MIT Python 3.8+ FastAPI

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

Advanced Configuration

🔌 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

PMCP Server
├── Admin Interface (Port 8080)
│   ├── Dashboard & Monitoring
│   ├── Plugin Management
│   ├── File Browser
│   └── Configuration
├── API Server (Port 8000)
│   ├── MCP Protocol Endpoints
│   ├── AI Service Integration
│   ├── Memory & Context Management
│   └── RAG & Document Processing
├── SSH/SFTP Server (Port 2222)
│   ├── Secure File Access
│   └── Remote Command Execution
└── Background Services
    ├── Plugin Manager
    ├── Database Connections
    ├── Logging System
    └── Monitoring Agents

🤝 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


PMCP - Empowering AI assistants with comprehensive context and capabilities 🚀

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