A comprehensive Model Context Protocol (MCP) server implementation that serves as both a learning resource and production-ready system. This project demonstrates best practices in AI system integration while providing educational content about MCP concepts.
- Educational Resource: Learn about Model Context Protocol (MCP) through hands-on implementation
 - Portfolio Development: Demonstrate professional software engineering practices
 - Production System: Build a robust MCP server implementation
 
- Model Context Protocol server implementation
 - Data storage and retrieval system
 - Real-time metrics and monitoring
 - Command execution framework
 - Analysis and reporting tools
 
- Detailed MCP concept explanations
 - Step-by-step tutorials
 - Commented implementation examples
 - Integration guides
 
- Python 3.8+
 - pip
 - virtualenv (recommended)
 
# Clone the repository
git clone https://github.com/mysterium-coniunctionis/mcp-system.git
cd mcp-system
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt- 
MCP Fundamentals
- What is Model Context Protocol?
 - Core concepts and architecture
 - Basic implementation patterns
 
 - 
System Components
- Server implementation
 - Data storage
 - Monitoring and metrics
 - Command execution
 
 - 
Advanced Topics
- Custom extensions
 - Performance optimization
 - Security considerations
 - Production deployment
 
 
mcp-system/
├── docs/               # Documentation and tutorials
├── examples/           # Example implementations
├── mcp_system/        # Core implementation
├── tests/             # Test cases
└── tutorials/         # Step-by-step guides
pytest tests/python -m mcp_system.server --dev- Basic MCP server implementation
 - Core documentation
 - Basic monitoring features
 - Command execution framework
 
- Advanced monitoring capabilities
 - Extended documentation
 - Performance optimizations
 - Security enhancements
 
- Production deployment guide
 - Load testing and benchmarks
 - Additional integrations
 - Community contributions
 
Contributions are welcome! Please read our Contributing Guide for details on our code of conduct and the process for submitting pull requests.
Detailed documentation is available in the /docs directory, including:
- Architecture overview
 - API documentation
 - Implementation guides
 - Best practices
 
This project is licensed under the MIT License - see the LICENSE file for details.
If you have any questions or need help with development, please open an issue or contribute to discussions.