MCP Codebase Insight

# System Architecture > 🚧 **Documentation In Progress** > > This documentation is being actively developed. More details will be added soon. ## Overview This document provides a comprehensive overview of the MCP Codebase Insight system architecture. For detailed workflow information, please see the [Workflows Documentation](../workflows/README.md). ## Architecture Components ### Core Systems - Vector Store System - Knowledge Base - Task Management - Health Monitoring - Error Handling - Metrics Collection - Cache Management ### Documentation - ADR Management - Documentation Tools - API Documentation ### Testing - Test Framework - SSE Testing - Integration Testing ## Detailed Documentation - [Core Components](../components/README.md) - [API Reference](../api/README.md) - [Development Guide](../development/README.md) ## System Overview This document provides a comprehensive overview of the MCP Codebase Insight system architecture, focusing on system interactions, dependencies, and design considerations. ## Core Systems ### 1. Vector Store System (`src/mcp_codebase_insight/core/vector_store.py`) - **Purpose**: Manages code embeddings and semantic search capabilities - **Key Components**: - Qdrant integration for vector storage - Embedding generation and management - Search optimization and caching - **Integration Points**: - Knowledge Base for semantic understanding - Cache Management for performance optimization - Health Monitoring for system status ### 2. Knowledge Base (`src/mcp_codebase_insight/core/knowledge.py`) - **Purpose**: Central repository for code insights and relationships - **Key Components**: - Pattern detection and storage - Relationship mapping - Semantic analysis - **Feedback Loops**: - Updates vector store with new patterns - Receives feedback from code analysis - Improves pattern detection over time ### 3. Task Management (`src/mcp_codebase_insight/core/tasks.py`) - **Purpose**: Handles async operations and job scheduling - **Key Components**: - Task scheduling and prioritization - Progress tracking - Resource management - **Bottleneck Mitigation**: - Task queuing strategies - Resource allocation - Error recovery ### 4. Health Monitoring (`src/mcp_codebase_insight/core/health.py`) - **Purpose**: System health and performance monitoring - **Key Components**: - Component status tracking - Performance metrics - Alert system - **Feedback Mechanisms**: - Real-time status updates - Performance optimization triggers - System recovery procedures ### 5. Error Handling (`src/mcp_codebase_insight/core/errors.py`) - **Purpose**: Centralized error management - **Key Components**: - Error classification - Recovery strategies - Logging and reporting - **Resilience Features**: - Graceful degradation - Circuit breakers - Error propagation control ## System Interactions ### Critical Paths 1. **Code Analysis Flow**: ```mermaid sequenceDiagram participant CA as Code Analysis participant KB as Knowledge Base participant VS as Vector Store participant CM as Cache CA->>VS: Request embeddings VS->>CM: Check cache CM-->>VS: Return cached/null VS->>KB: Get patterns KB-->>VS: Return patterns VS-->>CA: Return analysis ``` 2. **Health Monitoring Flow**: ```mermaid sequenceDiagram participant HM as Health Monitor participant CS as Component State participant TM as Task Manager participant EH as Error Handler HM->>CS: Check states CS->>TM: Verify tasks TM-->>CS: Task status CS-->>HM: System status HM->>EH: Report issues ``` ## Performance Considerations ### Caching Strategy - Multi-level caching (memory and disk) - Cache invalidation triggers - Cache size management ### Scalability Points 1. Vector Store: - Horizontal scaling capabilities - Batch processing optimization - Search performance tuning 2. Task Management: - Worker pool management - Task prioritization - Resource allocation ## Error Recovery ### Failure Scenarios 1. Vector Store Unavailable: - Fallback to cached results - Graceful degradation of search - Automatic reconnection 2. Task Overload: - Dynamic task throttling - Priority-based scheduling - Resource reallocation ## System Evolution ### Extension Points 1. Knowledge Base: - Plugin system for new patterns - Custom analyzers - External integrations 2. Monitoring: - Custom metrics - Alert integrations - Performance profiling ## Next Steps 1. **Documentation Needs**: - Detailed component interaction guides - Performance tuning documentation - Deployment architecture guides 2. **System Improvements**: - Enhanced caching strategies - More robust error recovery - Better performance monitoring