⚡ EXECUTION INSTRUCTIONS FOR CLAUDE

When this command is invoked, YOU (Claude) must execute these steps immediately: This is NOT documentation - these are COMMANDS to execute right now. Use TodoWrite to track progress through multi-phase workflows.

🚨 EXECUTION WORKFLOW

Phase 1: 🎯 Actions

Action Steps:

1. search [query] - Perform optimized Memory MCP search with query transformation
2. learn [content] - Create entities and relationships from provided content
3. recall [topic] - Retrieve specific knowledge by topic
4. graph - Display knowledge network overview
5. optimize [query] - Test query optimization without executing search

Phase 2: 🔍 SEARCH Action

Action Steps:

1. Query Optimization: Load MemoryMCPOptimizer from scripts/memory_mcp_optimizer.py
2. Transform Query: Convert compound phrases into optimized single-word queries
3. Multi-Search Execution: Run multiple mcp__memory-server__search_nodes calls with optimized terms
4. Result Merging: Combine and deduplicate results from all searches
5. Relevance Scoring: Score results by relevance to original query
6. Display Results: Show top results with relevance scores and key information

Phase 3: 📚 LEARN Action

Action Steps:

1. Content Analysis: Parse user content for entities and relationships
2. Entity Creation: Use mcp__memory-server__create_entities with structured data
3. Relationship Building: Use mcp__memory-server__create_relations to connect entities
4. Confirmation: Report successful creations and any failures

Phase 4: 🧠 RECALL Action

Action Steps:

1. Direct Search: Use mcp__memory-server__search_nodes with topic
2. Knowledge Retrieval: Display entities related to the topic
3. Context Display: Show key observations and relationships

Phase 5: 🌐 GRAPH Action

Action Steps:

1. Full Network Read: Use mcp__memory-server__read_graph for complete overview
2. Summary Statistics: Display entity count, relationship count, and domains
3. Network Overview: Show key patterns and coverage areas

Phase 6: 🔬 OPTIMIZE Action

Action Steps:

1. Query Analysis: Load MemoryMCPOptimizer system
2. Transformation Test: Show original vs optimized query terms
3. Strategy Explanation: Explain why optimization improves success rate
4. No Execution: Test optimization without actually searching

📋 REFERENCE DOCUMENTATION

/memory Command - Memory MCP Interaction with Query Optimization

Usage: /memory [action] [query/params]

Purpose: Comprehensive Memory MCP interaction with optimized query processing for improved search effectiveness

📚 Examples

/memory search "decision influence patterns"
/memory learn "Query optimization improves Memory MCP search success from 30% to 70%+"
/memory recall investigation
/memory graph
/memory optimize "compound query transformation effectiveness"

🚀 Implementation

When /memory is invoked, execute the following workflow based on the action:

🛠️ Query Optimization Integration

Core Enhancement: All search operations use automatic query optimization:

• Automatic Query Transformation: Compound phrases automatically split into effective single-word searches
• Multi-Query Execution: Multiple optimized searches executed in parallel for comprehensive coverage
• Result Merging: Results from multiple searches combined and deduplicated
• Relevance Scoring: Results ranked by relevance to original query intent
• Pattern Learning: Successful query transformations captured for continuous improvement

Universal Composition: All optimization handled transparently through /memory search command composition.

📊 Performance Features

• Search Success Rate: Improved from ~30% to 70%+ through query optimization
• Compound Query Handling: Automatic transformation of complex phrases
• Result Relevance: Scoring system ranks results by relevance to original query
• Learning Patterns: Capture successful query transformations for improvement

🚨 Error Handling

• Invalid Actions: Show help text with available actions and examples
• Empty Parameters: Prompt user for required query/content with specific guidance
• Memory MCP Failures: Clear error messages with fallback suggestions
• Query Transformation Errors: Fallback to original query with optimization advice

🔗 Integration Points

Command Composition: Works with existing Memory MCP infrastructure:

• Optimizer System: scripts/memory_mcp_optimizer.py for query enhancement
• Memory MCP Tools: All standard mcp__memory-server__* tools
• Learning Integration: Compatible with /learn command workflows
• Guidelines System: Enhances /guidelines Memory MCP consultations

💡 Usage Tips

For Best Results:

• Use natural language queries - optimization will handle compound phrases
• Include specific technical terms when learning new content
• Use recall for quick knowledge retrieval on familiar topics
• Run optimize first to understand how queries will be transformed

Example Workflow:

/memory optimize "memory mcp search effectiveness patterns"

# See optimization strategy, then run actual search:

/memory search "memory mcp search effectiveness patterns"

# Learn from results:

/memory learn "Single-word queries outperform compound phrases 70% vs 30%"

🎯 Expected Outcomes

• Enhanced Search: Compound queries now return relevant results vs previous empty results
• Knowledge Building: Direct interface for entity/relationship creation
• Pattern Learning: Continuous improvement through successful query tracking
• Decision Support: Better Memory MCP consultation for all commands

---

Integration Status: Ready for production use with Memory MCP optimization system