CLP MCP - DevOps Infrastructure Server

# Implementation Summary: Dynamic Long-Term Memory System ## Overview Successfully implemented a comprehensive dynamic long-term memory system for the CLP MCP server that leverages all available information sources from past and present to provide intelligent, informed decision-making and next steps. ## What Was Implemented ### Core Memory System (`src/memory/index.ts`) 1. **LongTermMemory Class** (484 lines) - Advanced memory storage with rich metadata - Multiple indexing strategies (context, tags, timeline) - Intelligent retrieval with relevance scoring - Automatic relationship detection between memories - Pattern recognition and consolidation - Statistical tracking and analytics 2. **Key Features** - ✅ Temporal awareness (timestamps, access tracking) - ✅ Context-based organization - ✅ Multi-tag categorization - ✅ Importance scoring (0-1 scale) - ✅ Custom metadata support - ✅ Related memory linking - ✅ Relevance-based ranking - ✅ Pattern detection - ✅ Memory consolidation ### MCP Integration (`src/server/index.ts`) 1. **7 Memory Tools** - `memory_store` - Store memories with rich metadata - `memory_recall` - Advanced multi-criteria recall - `memory_search` - Full-text search - `memory_get_recent` - Recent memories - `memory_get_important` - High-importance memories - `memory_stats` - System statistics - `memory_consolidate` - Pattern analysis and summarization 2. **4 Memory Resources** - `memory://all` - All stored memories - `memory://stats` - System statistics - `memory://recent` - Recent memories (20) - `memory://important` - Important memories (20) ### Intelligent Ranking Algorithm **Relevance Scoring** (for search queries): - Content matching: 1.0 base score - Tag matching: 0.5 per matching tag - Context matching: 0.5 bonus - Importance boost: ×(1 + importance) - Access frequency boost: ×(1 + log(accessCount + 1) × 0.1) **Dynamic Scoring** (for general recall): - Recency: 40% weight (exponential decay, 1-week half-life) - Importance: 40% weight (user-defined) - Access frequency: 20% weight (normalized to 0-1) ### Documentation 1. **MEMORY_SYSTEM.md** (8.7KB) - Complete system architecture - API reference for all tools - Usage examples - Best practices - Use cases - Implementation details 2. **Updated README.md** - Feature highlights - Quick start guide - Links to documentation 3. **test-memory.ts** - Comprehensive test suite - 11 test scenarios - Real-world usage examples ## Test Results All 11 tests passed successfully: - ✅ Memory storage with metadata - ✅ Statistics tracking (5 memories, 4 contexts, 13 tags) - ✅ Full-text search (found 2/2 matches) - ✅ Context filtering (2 development memories) - ✅ Tag filtering (1 bug memory) - ✅ Recent retrieval (3 most recent) - ✅ Importance filtering (3 memories >= 0.8) - ✅ Multi-criteria recall (1 match) - ✅ Pattern consolidation - ✅ Memory updates - ✅ Export functionality ## Key Capabilities ### 1. Information Synthesis The system can combine information from: - Past experiences (stored memories) - Current context (recent memories) - Related information (linked memories) - Pattern recognition (consolidation) ### 2. Intelligent Prioritization Automatically prioritizes information based on: - Temporal relevance (recency) - Explicit importance (user-set) - Implicit importance (access frequency) - Contextual relevance (matching criteria) ### 3. Dynamic Learning The system learns over time through: - Access count tracking - Relationship discovery - Pattern identification - Relevance adjustments ### 4. Multi-dimensional Organization Information is organized by: - Context (development, planning, support, etc.) - Tags (technology, type, priority, etc.) - Time (creation and access timestamps) - Importance (0-1 scale) - Relationships (linked memories) ## Usage Examples ### Store Critical Information \`\`\`typescript memory_store({ content: "Fixed critical bug in authentication system", context: "development", tags: ["bugfix", "security", "critical"], importance: 0.95, metadata: { commit: "abc123", issue: "#456" } }) \`\`\` ### Intelligent Recall \`\`\`typescript memory_recall({ query: "authentication security", context: "development", minImportance: 0.7, limit: 5 }) \`\`\` ### Pattern Recognition \`\`\`typescript memory_consolidate({ context: "development" }) // Returns: patterns found, summary of important memories \`\`\` ## Performance Characteristics - **Storage**: O(1) insertion - **Search**: O(n) with intelligent ranking - **Context/Tag lookup**: O(1) with indices - **Related memory detection**: O(n) per store operation - **Memory footprint**: Efficient with Map/Set data structures ## Future Enhancement Opportunities 1. **Persistence**: Database or file-based storage 2. **Vector Search**: Semantic similarity with embeddings 3. **Memory Decay**: Automatic importance adjustment over time 4. **AI Summarization**: LLM-powered memory summaries 5. **Cross-session**: Shared memory across users/sessions 6. **Advanced Analytics**: Usage patterns, insights, recommendations ## Benefits Delivered 1. **Comprehensive Information Access**: All past and present information available 2. **Intelligent Prioritization**: Most relevant information surfaces first 3. **Pattern Recognition**: Identify trends and recurring themes 4. **Context Awareness**: Information organized by logical categories 5. **Temporal Intelligence**: Recent and frequently-accessed information prioritized 6. **Relationship Discovery**: Related information automatically linked 7. **Extensibility**: Easy to add new features and integrations ## Code Quality - ✅ TypeScript with full type safety - ✅ Zod schemas for validation - ✅ Comprehensive inline documentation - ✅ Clean, modular architecture - ✅ Tested and verified functionality - ✅ Production-ready build ## Files Changed/Added 1. `src/memory/index.ts` - New (484 lines) 2. `src/server/index.ts` - Modified (536 lines) 3. `MEMORY_SYSTEM.md` - New (8.7KB documentation) 4. `README.md` - Updated (feature highlights) 5. `test-memory.ts` - New (test suite) ## Conclusion The dynamic long-term memory system is now fully implemented, tested, and documented. It provides intelligent storage, retrieval, and analysis of information across all time periods, enabling informed decision-making and contextual awareness for the CLP MCP server. The system is production-ready and can be further enhanced with persistence, vector search, and AI-powered features as needed.