MCP Self-Learning Server

# MCP Self-Learning Server Architecture ## 🏗️ System Overview The MCP Self-Learning Server is an autonomous learning system that integrates with multiple AI agents and services to continuously improve performance through pattern recognition and knowledge accumulation. ## 🧠 Core Components ### 1. Learning Engine **Location**: `mcp-self-learning-server.js` (LearningEngine class) - **Pattern Recognition**: Analyzes interaction patterns using semantic embeddings - **Feature Extraction**: Extracts contextual cues, performance metrics, and temporal patterns - **Memory Management**: Manages up to 1000 patterns in memory with LRU eviction - **Learning Cycles**: Periodic optimization and knowledge consolidation ### 2. Knowledge Synchronizer **Location**: `mcp-self-learning-server.js` (KnowledgeSynchronizer class) - **Cross-Service Learning**: Shares knowledge between different AI services - **Data Persistence**: Auto-saves learning data every 5 minutes - **Export/Import**: Supports knowledge transfer between instances - **Backup Management**: Maintains rotating backups of learning data ### 3. MCP Server Interface **Location**: `mcp-self-learning-server.js` (SelfLearningMCPServer class) - **MCP Protocol**: Implements Model Context Protocol for tool registration - **Tool Definitions**: Provides learning tools for external agents - **Request Handling**: Processes learning requests from connected agents - **Resource Management**: Manages server resources and connections ## 🌐 Integration Architecture ### Client Libraries #### Node.js Client (`lib/self-learning-client.js`) ```javascript import SelfLearningClient from './lib/self-learning-client.js'; const client = new SelfLearningClient({ port: 8765 }); await client.connect(); ``` #### Python Client (`lib/self_learning_client.py`) ```python from lib.self_learning_client import SelfLearningClient client = SelfLearningClient(base_url="http://localhost:8765") await client.connect() ``` ### REST API (`api/rest-server.js`) - **Endpoints**: `/analyze`, `/insights`, `/optimize`, `/predict`, `/metrics` - **WebSocket**: Real-time updates on `ws://localhost:8765/ws` - **CORS Support**: Cross-origin requests enabled - **Authentication**: Token-based auth (configurable) ### CLI Tool (`bin/mcp-learn.js`) ```bash mcp-learn start # Start MCP server mcp-learn api # Start REST API mcp-learn analyze # Analyze patterns mcp-learn insights # Get learning insights ``` ## 🔄 Data Flow ### 1. Pattern Analysis Flow ``` Agent Interaction → Feature Extraction → Pattern Matching → Learning Update → Knowledge Storage ``` ### 2. Learning Cycle Flow ``` Accumulated Patterns → Analysis → Optimization → Knowledge Consolidation → Performance Improvement ``` ### 3. Integration Flow ``` External Agent → MCP Tool Call → Learning Engine → Pattern Analysis → Recommendations → Agent ``` ## 🏗️ Integration Patterns ### Claudio (AIMCP Orchestrator) **Integration**: `integrations/claudio-mcp-tools.js` - **6 MCP Tools**: learn_from_interaction, get_learning_insights, optimize_workflow, etc. - **Workflow Learning**: Learns from multi-agent workflow outcomes - **Predictive Suggestions**: Predicts next best actions in workflows ### Claudia (Voice Assistant) **Integration**: `integrations/claudia_learning_plugin.py` - **Voice Interaction Learning**: Analyzes voice commands and responses - **Intent Recognition**: Improves intent detection accuracy over time - **Conversation Optimization**: Suggests better response patterns ## 📊 Performance Characteristics ### Learning Engine Performance - **Startup Time**: ~50ms - **Pattern Analysis**: <100ms per interaction - **Memory Efficiency**: ~85MB RSS for 1000+ patterns - **Persistence**: Auto-save every 5 minutes ### API Performance - **Response Time**: <100ms average - **WebSocket Latency**: <10ms - **Concurrent Connections**: Supports 100+ simultaneous - **Throughput**: 1000+ requests/minute ## 🔧 Configuration Architecture ### Server Configuration ```json { "maxMemorySize": 1000, "persistenceEnabled": true, "autoSaveInterval": 300000, "logLevel": "info" } ``` ### Integration Configuration - **Claudio**: Defined in `/home/ben/saralegui-solutions-llc/claude-assistant/config/claudio.config.json` - **Claudia**: Plugin-based configuration - **Systemd**: Service configuration in `~/.config/systemd/user/` ## 🔐 Security Architecture ### Data Security - **Local Storage**: All data stored locally in `data/` directory - **No External Dependencies**: No external API calls for core functionality - **Configurable Auth**: Optional API authentication - **Process Isolation**: Runs as user service ### Network Security - **Local Binding**: Default binding to localhost only - **Optional CORS**: Configurable cross-origin policies - **WebSocket Security**: Optional authentication for WebSocket connections - **Rate Limiting**: Configurable request rate limits ## 📈 Scalability Architecture ### Horizontal Scaling - **Multiple Instances**: Can run multiple learning servers - **Knowledge Sharing**: Cross-instance knowledge synchronization - **Load Distribution**: REST API can be load balanced ### Vertical Scaling - **Memory Management**: Configurable memory limits - **Background Processing**: Non-blocking learning cycles - **Resource Monitoring**: Built-in performance metrics ## 🔄 Extension Points ### Custom Learning Algorithms - **Pattern Extractors**: Custom feature extraction algorithms - **Learning Strategies**: Pluggable learning strategies - **Optimization Functions**: Custom optimization algorithms ### Integration Extensions - **New Agent Types**: Easy integration with new AI agents - **Custom Tools**: Extensible MCP tool definitions - **Protocol Extensions**: Support for new communication protocols ## 📋 Dependencies ### Core Dependencies - `@modelcontextprotocol/sdk`: MCP protocol implementation - `express`: REST API server - `ws`: WebSocket support - `commander`: CLI interface ### Development Dependencies - `axios`: HTTP client for testing - Testing framework: Custom test implementations ## 🚀 Deployment Architecture ### Local Development - **Direct Execution**: `node mcp-self-learning-server.js` - **CLI Development**: `mcp-learn start` ### Production Deployment - **Systemd Service**: Auto-start with system boot - **Global CLI**: NPM global package installation - **Docker**: Containerized deployment option - **Kubernetes**: Pod deployment with persistent volumes This architecture provides a scalable, secure, and extensible foundation for autonomous learning across multiple AI agent systems.