marm-mcp

# MARM MCP Server - Advanced Workflows & Migration ## Cross-App Memory Strategies, Migration Guide, and Best Practices **MARM v2.2.6** - Universal MCP Server for AI Memory Intelligence --- ## Cross-App Memory Strategies ### Multi-LLM Session Organization **Strategy**: Use LLM-specific session names to track contributions: ```txt Sessions: - claude-code-review-2025-01 - qwen-research-analysis-2025-01 - gemini-creative-writing-2025-01 - cross-ai-project-planning-2025-01 ``` ### Memory Sharing Workflow 1. **Individual Sessions**: Each AI works in named sessions 2. **Cross-Pollination**: Use `marm_smart_recall` to find relevant insights 3. **Synthesis Sessions**: Create shared sessions where AIs build on each other's work --- ## Pro Tips & Best Practices ### Memory Management **Log Compaction**: Use `marm_summary`, delete entries, replace with summary **Session Naming**: Include LLM name for cross-referencing **Strategic Logging**: Focus on key decisions, solutions, discoveries, configurations ### Search Strategies **Global Search**: Use `search_all=True` to search across all sessions **Natural Language Search**: "authentication problems with JWT tokens" vs "auth error" **Temporal Search**: Include timeframes in queries ### Workflow Optimization **Notebook Stacking**: Combine multiple entries for complex workflows **Session Lifecycle**: Start → Work → Reference → End with compaction --- ## Advanced Workflows ### Project Memory Architecture ```txt Project Structure: ├── project-name-planning/ # Initial design and requirements ├── project-name-development/ # Implementation details ├── project-name-testing/ # QA and debugging notes ├── project-name-deployment/ # Production deployment └── project-name-retrospective/ # Lessons learned ``` ### Knowledge Base Development 1. **Capture**: Use `marm_contextual_log` for new learnings 2. **Organize**: Create themed sessions for knowledge areas 3. **Synthesize**: Regular `marm_summary` for knowledge consolidation 4. **Apply**: Convert summaries to `marm_notebook_add` entries ### Multi-AI Collaboration Pattern ```txt Phase 1: Individual Research - Each AI works in dedicated sessions - Focus on their strengths (Claude=code, Qwen=analysis, Gemini=creativity) Phase 2: Cross-Pollination - Use marm_smart_recall to find relevant insights - Build upon previous work Phase 3: Synthesis - Create collaborative sessions - Combine insights for comprehensive solutions ``` --- ## Migration from MARM Commands ### Transitioning from Text-Based MARM If you're familiar with the original text-based MARM protocol, the MCP server provides enhanced capabilities while maintaining familiar workflows: **Command Mapping**: | Chatbot Command | MCP Equivalent | How It Works | | -------------------- | ------------------- | --------------------------------------------- | | `/start marm` | `marm_start` | Claude calls automatically when needed | | `/refresh marm` | `marm_refresh` | Claude calls to maintain protocol adherence | | `/log session: name` | `marm_log_session` | Claude organizes work into sessions | | `/log entry: details`| `marm_log_entry` | Claude logs milestones and decisions | | `/summary: session` | `marm_summary` | Claude generates summaries on request | | `/notebook add: item`| `marm_notebook_add` | Claude stores reference information | | Manual memory search | `marm_smart_recall` | Claude searches semantically | ### Key Improvements in MCP Version **Enhanced Memory System**: - Semantic search replaces keyword matching - Cross-app memory sharing between AI clients - Automatic content classification - Data storage with SQLite **Advanced Features**: - Multi-AI collaboration workflows - Global search with `search_all=True` - Context bridging between topics - System health monitoring ### Migration Tips 1. **Session Organization**: Use descriptive session names instead of manual date tracking 2. **Memory Management**: Leverage auto-classification instead of manual categorization 3. **Notebook System**: Convert text-based instructions to structured notebook entries 4. **Search Strategy**: Use natural language queries instead of exact keywords ### Backward Compatibility The MCP server maintains full compatibility with existing MARM concepts: - Same core commands with enhanced capabilities - Familiar logging and notebook workflows - Consistent memory management principles - Enhanced performance and reliability --- ## Troubleshooting ### Memory Not Finding Expected Results **Solution**: Check content classification - your memory might be in a different category **Tool**: Use `marm_log_show` to browse all entries manually ### Session Confusion **Solution**: Use `marm_current_context` to check current session **Prevention**: Always name sessions descriptively ### Performance Issues **Solution**: Use log compaction - `marm_summary` followed by entry cleanup **Tool**: `marm_system_info` to check database statistics ### Lost Context **Solution**: `marm_refresh` to reset MARM behavior **Recovery**: `marm_smart_recall` to find related previous conversations --- ## FAQ ### General Usage **Q: How is MARM different from basic AI memory?** A: MARM uses semantic understanding, not keyword matching. It finds related concepts even with different wording and works across multiple AI applications. **Q: Can I use MARM with multiple AI clients simultaneously?** A: Yes! MARM is designed for cross-app memory sharing. Each AI can access and contribute to the same memory store. **Q: How much memory can MARM store?** A: No hard limits - MARM uses efficient SQLite storage with semantic embeddings. Typical usage stores thousands of memories without performance issues. ### Memory Management FAQ **Q: When should I create a new session vs. continuing an existing one?** A: Create new sessions for distinct topics, projects, or time periods. Continue existing sessions for related work or follow-up discussions. **Q: How does auto-classification work?** A: MARM analyzes content using semantic models to determine if it's code, project work, book/research material, or general conversation. **Q: Can I search across all sessions or just one?** A: Both! `marm_smart_recall` can search globally (default) or within specific sessions using the session_name parameter. ### Technical Questions **Q: What happens if MARM server is offline?** A: Your AI client will work normally but without memory features. Memory resumes when MARM reconnects - no data loss. **Q: How does semantic search work?** A: MARM converts text to vector embeddings using sentence-transformers, then finds similar content using vector similarity rather than exact word matching. **Q: Can I backup my MARM memory?** A: Yes - MARM uses SQLite databases stored locally. Back up the `~/.marm/` directory to preserve all memories. ### Best Practices FAQ **Q: How often should I use log compaction?** A: At the end of significant sessions (5+ entries) or weekly for ongoing projects. This keeps memory efficient while preserving insights. **Q: Should I log everything or be selective?** A: Be selective - log decisions, solutions, insights, and key information. Avoid logging routine conversations or easily recreated content. **Q: How do I organize memories for team collaboration?** A: Use consistent session naming (include dates, project names, contributor names) and leverage cross-session search to find team insights. ### Integration & Setup FAQ **Q: Which AI clients work with MARM?** A: Any MCP-compatible client: Claude Code, Qwen CLI, Gemini CLI, and other Model Context Protocol implementations. **Q: Do I need to restart MARM when switching between AI clients?** A: No - MARM runs as a persistent service. Multiple AI clients can connect simultaneously to the same memory store. **Q: How do I know if MARM is working correctly?** A: Use `marm_system_info` to check server status, database statistics, and loaded capabilities. Look for "operational" status and healthy database counts. >Built with ❤️ by MARM Systems - Universal MCP memory intelligence