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

Related MCP server: Rini MCP Server

🛠️ 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

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

• Issues: GitHub Issues

• Discussions: GitHub Discussions

• Documentation: Project Wiki

PMCP - Empowering AI assistants with comprehensive context and capabilities 🚀