MCP Memory

# Claude Integration Guide for MCP Memory ## 🎯 Project Vision This project is evolving from a simple memory storage system into an intelligent coding philosophy knowledge base that learns and improves over time. The goal is to create a system that proactively helps developers by surfacing relevant patterns, preferences, and solutions at the right moment. ## 🚀 How to Use This System ### At Session Start 1. **Always use the `session_start` prompt** to initialize your context 2. **Check the guide**: Read `memory://philosophy/guide` for usage patterns 3. **Review recent additions**: Check `memory://philosophy/recent` for latest patterns ### During Development 1. **Search before implementing**: Use `searchMCPMemory` with topics like "error handling", "testing", "api design" 2. **Store discoveries**: When you find a pattern that works, use `addToMCPMemory` to remember it 3. **Use prompts**: The system provides contextual prompts based on your activity ### Key Commands - `/search <topic>` - Search for relevant patterns - `/remember <pattern>` - Store a new discovery - `/philosophy` - Get contextual coding philosophy ## 📋 Proposed Next Steps ### 1. **Conversation Context Storage** (High Priority) - Store entire conversation threads with memories - Use OpenAI conversation format for compatibility - Enable AI to understand the full context of discoveries - Allow queries like "show me the conversation that led to this pattern" ### 2. **Enhanced AI Summarization** - Implement intelligent summarization of conversations - Extract key insights and decisions automatically - Generate concise "why" explanations for each pattern - Create learning paths from conversation histories ### 3. **Improved Search Intelligence** - Implement recursive search based on initial results - Add "did you mean" suggestions for common typos - Create search templates for common queries - Build a recommendation engine based on usage patterns ### 4. **Development Workflow Integration** - Add git hooks to capture commit patterns - Integrate with CI/CD to track what patterns lead to failures - Create project-specific memory spaces automatically - Build ADD/TDD transformation tracking ### 5. **Knowledge Graph Features** - Visualize relationships between memories - Show learning paths and pattern evolution - Create dependency graphs for patterns - Enable navigation through related concepts ### 6. **Export and Sharing** - Export memories as markdown documentation - Share pattern libraries with teams - Create public pattern repositories - Generate project-specific playbooks ### 7. **Metrics and Analytics** - Track which patterns are used most - Measure search effectiveness - Identify knowledge gaps - Generate learning recommendations ### 8. **ADD/TDD Pattern Library** - Store TPP transformations with examples - Track which transformations lead to which patterns - Build a library of test-driven development patterns - Create transformation templates ## 🔧 Technical Improvements ### Performance - Implement caching for frequent searches - Optimize vector search with better indexing - Add pagination for large result sets - Implement background processing for categorization ### Data Quality - Add duplicate detection and merging - Implement memory decay (old, unused memories fade) - Create quality scores for memories - Add community voting/validation ### Integration - Create VS Code extension - Build CLI tool for command-line access - Add webhook support for external tools - Create REST API for programmatic access ## 🎓 Learning from Usage The system should continuously improve by: 1. Learning which expansions work best 2. Identifying common query patterns 3. Understanding user preferences from interactions 4. Building better categorization models 5. Improving relevance scoring based on feedback ## 🚦 Implementation Priority 1. **Immediate** (This Week) - Test and refine current intelligent features - Gather usage data to validate approach - Fix any bugs in query expansion or scoring 2. **Short Term** (Next Month) - Implement conversation context storage - Add AI summarization - Build recursive search 3. **Medium Term** (Next Quarter) - Create knowledge graph visualization - Build export/sharing features - Implement ADD/TDD pattern tracking 4. **Long Term** (Next 6 Months) - Full workflow integration - Analytics dashboard - Community features ## 💡 Key Insight The most valuable aspect of this system is that it learns from actual usage. Every search, every stored memory, and every interaction makes it smarter. The goal is to reach a point where the system can predict what you need before you ask for it. Remember: **Always search the coding philosophy before starting any new task!**