JetsonMCP

# JetsonMCP Architecture This document describes the technical architecture and design decisions behind JetsonMCP. ## Overview JetsonMCP is built using a hexagonal (ports and adapters) architecture that separates business logic from external concerns. This design allows for easy testing, maintainability, and extensibility. ## Core Components ### 1. MCP Server (`server.py`) **Responsibility**: Main orchestration layer that handles MCP protocol communication and coordinates between tools. **Key Features**: - MCP protocol compliance - Tool registration and routing - Resource management - Error handling and recovery ### 2. Configuration Management (`config.py`) **Responsibility**: Centralized configuration handling with validation and type safety. **Features**: - Pydantic-based validation - Environment variable loading - Configuration sections for different domains - Runtime configuration validation ### 3. Tool Architecture (`tools/`) **Base Tool (`base.py`)**: - Abstract base class for all tools - SSH connection management - Common command execution patterns - Resource cleanup handling **Specialized Tools**: - `HardwareTool`: Power management, temperature monitoring, GPIO control - `AIWorkloadsTool`: ML framework management, model deployment - `ContainersTool`: Docker and GPU container management - `SystemTool`: Basic system administration - Additional tools for specific domains ### 4. Utility Layer (`utils/`) **Logger (`logger.py`)**: - Centralized logging configuration - Multiple output handlers (console, file) - Level-based filtering ## Design Patterns ### 1. Command Pattern Each tool implements a command pattern where: - Actions are discrete, named operations - Parameters are validated using JSON schemas - Results are consistently structured ```python async def execute(self, tool_name: str, arguments: Dict[str, Any]) -> List[Any]: action = arguments.get("action") if action == "get_temperature": return await self._get_temperature() # ... other actions ``` ### 2. Factory Pattern The server uses a factory pattern to instantiate and manage tools: ```python def _setup_tools(self) -> None: self._tools["hardware"] = HardwareTool(self.config) self._tools["ai_workloads"] = AIWorkloadsTool(self.config) # ... other tools ``` ### 3. Strategy Pattern SSH connection management uses different strategies based on configuration: - Password authentication - Key-based authentication - Mock connections for testing ### 4. Observer Pattern Resource monitoring and state management use observer patterns for: - Temperature alerts - Resource utilization thresholds - System state changes ## Data Flow ``` ┌─────────────────┐ ┌──────────────┐ ┌─────────────────┐ │ Claude AI │───▶│ MCP Server │───▶│ Tool Router │ │ │ │ │ │ │ └─────────────────┘ └──────────────┘ └─────────────────┘ │ ▼ ┌─────────────────┐ ┌──────────────┐ ┌─────────────────┐ │ Jetson Nano │◀───│ SSH Client │◀───│ Specific Tool │ │ │ │ │ │ │ └─────────────────┘ └──────────────┘ └─────────────────┘ ``` 1. **Request Phase**: - Claude sends MCP request to server - Server validates and routes to appropriate tool - Tool executes SSH commands on Jetson 2. **Processing Phase**: - Commands execute on Jetson hardware - Results are processed and structured - Error handling and recovery 3. **Response Phase**: - Structured data returned through tool - Server formats MCP response - Claude receives actionable information ## Error Handling Strategy ### 1. Layered Error Handling ``` Application Layer ──▶ User-friendly messages │ Tool Layer ──▶ Domain-specific errors │ SSH Layer ──▶ Connection/command errors │ Transport Layer ──▶ Network/protocol errors ``` ### 2. Error Types - **Configuration Errors**: Invalid settings, missing credentials - **Connection Errors**: SSH failures, network issues - **Command Errors**: Failed command execution, permission issues - **Hardware Errors**: Device-specific failures, thermal issues ### 3. Recovery Strategies - **Retry Logic**: Exponential backoff for transient failures - **Fallback Commands**: Alternative approaches for common operations - **Graceful Degradation**: Partial functionality when components fail ## Security Architecture ### 1. Authentication - SSH key-based authentication (preferred) - Password authentication (fallback) - Host key verification - Connection timeouts ### 2. Authorization - Sudo privilege management - Command validation and sanitization - Path traversal protection - Resource access controls ### 3. Data Protection - Credential encryption in transit - Secure credential storage - Audit logging - Session management ## Performance Considerations ### 1. Connection Pooling - SSH connection reuse - Connection health monitoring - Automatic reconnection - Resource cleanup ### 2. Command Optimization - Batched operations where possible - Caching for expensive operations - Parallel execution for independent tasks - Result streaming for large outputs ### 3. Resource Management - Memory-conscious data structures - Lazy loading for large datasets - Garbage collection optimization - Connection lifecycle management ## Testing Strategy ### 1. Test Categories - **Unit Tests**: Individual function testing - **Integration Tests**: Tool interaction testing - **End-to-End Tests**: Full workflow testing - **Performance Tests**: Load and stress testing ### 2. Mock Strategy ```python # Mock SSH connections for unit testing if self.config.test_mode and self.config.mock_ssh_connections: return {"stdout": "mock output", "stderr": "", "return_code": 0} ``` ### 3. Test Infrastructure - Pytest-based test suite - Docker containers for isolation - CI/CD integration - Coverage reporting ## Extensibility ### 1. Adding New Tools 1. Inherit from `BaseTool` 2. Implement required abstract methods 3. Register in server configuration 4. Add tests and documentation ### 2. Hardware Platform Support - Abstract hardware interfaces - Platform-specific implementations - Feature detection and fallbacks - Configuration-driven behavior ### 3. Protocol Extensions - MCP resource providers - Custom message types - Streaming capabilities - Notification systems ## Deployment Architecture ### 1. Development Setup ``` Developer Machine Jetson Nano ┌───────────────┐ ┌──────────────┐ │ Claude │ │ │ │ Desktop │ │ SSH Server │ │ │ │ │ │ JetsonMCP │───▶│ Hardware │ │ Server │ │ AI Worklds │ │ │ │ Containers │ └───────────────┘ └──────────────┘ ``` ### 2. Production Setup ``` Edge Network ┌─────────────────────────────────────────┐ │ Edge Gateway Jetson Cluster │ │ ┌─────────────┐ ┌──────────────┐ │ │ │ JetsonMCP │─────▶│ Jetson #1 │ │ │ │ Proxy │ │ Jetson #2 │ │ │ │ │ │ Jetson #3 │ │ │ └─────────────┘ └──────────────┘ │ └─────────────────────────────────────────┘ │ ▼ Cloud Management ┌──────────────┐ │ Monitoring │ │ Analytics │ │ Updates │ └──────────────┘ ``` ## Future Architecture Considerations ### 1. Scalability - Multi-Jetson management - Load balancing across devices - Distributed model serving - Edge cluster orchestration ### 2. AI/ML Pipeline Integration - MLOps workflow integration - Model versioning and deployment - A/B testing frameworks - Performance monitoring ### 3. IoT Integration - Sensor data ingestion - Real-time processing pipelines - Edge-to-cloud data sync - Device fleet management ## Conclusion JetsonMCP's architecture prioritizes: - **Modularity**: Clear separation of concerns - **Testability**: Comprehensive testing strategy - **Extensibility**: Easy addition of new capabilities - **Reliability**: Robust error handling and recovery - **Security**: Multi-layered security approach - **Performance**: Optimized for edge computing constraints This architecture provides a solid foundation for edge AI system management while remaining flexible enough to adapt to future requirements.