MCP Agent Tracker

# 🚀 Centralized Prompt Generator System ## Overview The **Centralized Prompt Generator System** is a unified, maintainable solution that consolidates all prompt generation logic from various files into one comprehensive system. It provides multiple enhancement strategies, better error handling, and more informative prompts. ## ✨ Key Features ### **🎯 Multiple Enhancement Strategies** - **Comprehensive**: Full context with all available information - **Technical**: Focused on technical details and best practices - **Conversation**: Emphasizes conversation flow and user preferences - **Smart**: Adaptive context based on detected patterns - **Minimal**: Essential context for quick responses ### **🔧 Centralized Architecture** - Single source of truth for all prompt generation - Consistent formatting and structure across all strategies - Easy maintenance and updates - Unified error handling and fallbacks ### **📊 Performance & Monitoring** - Intelligent caching with configurable TTL - Performance metrics and statistics - Cache hit/miss tracking - Generation time monitoring ### **🔄 Fallback Support** - Graceful degradation when dependencies fail - Maintains backward compatibility - Original implementations as fallbacks ## 🏗️ Architecture ``` ┌─────────────────────────────────────────────────────────────┐ │ Prompt Generator │ │ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────┐ │ │ │ Comprehensive │ │ Technical │ │ Conversation│ │ │ │ Strategy │ │ Strategy │ │ Strategy │ │ │ └─────────────────┘ └─────────────────┘ └─────────────┘ │ │ ┌─────────────────┐ ┌─────────────────┐ │ │ │ Smart │ │ Minimal │ │ │ │ Strategy │ │ Strategy │ │ │ └─────────────────┘ └─────────────────┘ │ └─────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────┐ │ Context Cache │ │ (5 min TTL) │ └─────────────────┘ │ ▼ ┌─────────────────┐ │ Context Data │ │ Gathering │ └─────────────────┘ ``` ## 🚀 Usage ### **Basic Usage** ```python from prompt_generator import prompt_generator # Generate comprehensive enhanced prompt enhanced = prompt_generator.generate_enhanced_prompt( user_message="How do I set up the database?", context_type="comprehensive" ) # Generate technical-focused prompt technical = prompt_generator.generate_enhanced_prompt( user_message="What's the best way to optimize this query?", context_type="technical" ) ``` ### **Convenience Functions** ```python from prompt_generator import ( generate_comprehensive_prompt, generate_technical_prompt, generate_conversation_prompt, generate_smart_prompt, generate_minimal_prompt ) # Quick access to different strategies comprehensive = generate_comprehensive_prompt("Your message here") technical = generate_technical_prompt("Your message here") ``` ### **Advanced Usage** ```python # Force refresh context (ignore cache) enhanced = prompt_generator.generate_enhanced_prompt( user_message="Your message", context_type="comprehensive", force_refresh=True ) # Get statistics stats = prompt_generator.get_stats() print(f"Success rate: {stats['success_rate']}") print(f"Cache hit rate: {stats['cache_hit_rate']}") # Clear cache prompt_generator.clear_cache() ``` ## 🔄 Integration ### **Updated Files** The following files now use the centralized prompt generator: 1. **`main.py`** - Main agent interaction function 2. **`smart_context_injector.py`** - Smart context injection 3. **`auto_context_wrapper.py`** - Auto context enhancement 4. **`local_mcp_server_simple.py`** - Local MCP server 5. **`cursor_agent_integration.py`** - Cursor integration ### **Fallback Behavior** Each integration includes fallback logic: ```python try: # Use centralized prompt generator from prompt_generator import prompt_generator enhanced = prompt_generator.generate_enhanced_prompt(message, "comprehensive") except ImportError: # Fallback to original implementation enhanced = original_prompt_generation(message) ``` ## 📊 Enhanced Prompt Structure ### **Comprehensive Strategy Example** ``` === 🚀 COMPREHENSIVE ENHANCED PROMPT === USER MESSAGE: How do I set up the database? === 📊 CONTEXT INJECTION === 🎯 CONVERSATION SUMMARY: Current conversation state: 15 total interactions... 📝 ACTION HISTORY: Recent actions: Database configuration, MCP server setup... ⚙️ TECH STACK: Python 3.x, SQLite database, MCP (Model Context Protocol)... 🎯 PROJECT PLANS & OBJECTIVES: 1. Build powerful conversation tracking system ✅ 2. Implement centralized prompt generation ✅ 3. Create comprehensive context injection... 👤 USER PREFERENCES: • Database Choice: SQLite over PostgreSQL • Communication Style: Technical but friendly • Tool Preferences: Built-in tools over external APIs 🤖 AGENT METADATA: Friendly name: Johny, Agent ID: mcp-project-001... 🔍 PROJECT PATTERNS: • Python development best practices • SQLite database patterns • Model Context Protocol integration ✅ BEST PRACTICES: • Follow DRY (Don't Repeat Yourself) principle • Implement proper error handling and logging • Write comprehensive tests for critical functionality ⚠️ COMMON ISSUES & SOLUTIONS: • Configuration and environment setup • Dependency management and version conflicts • Performance bottlenecks and optimization 🔄 DEVELOPMENT WORKFLOW: • Analyze requirements and context • Design comprehensive solution • Implement with best practices • Test and validate • Deploy and monitor • Maintain conversation continuity 📈 CONTEXT CONFIDENCE: 95.0% === 🎯 INSTRUCTIONS === Please respond to the user's message above, taking into account: 1. 📚 The current conversation context and recent interactions 2. 🎯 The specific actions and steps taken so far 3. ⚙️ The technical stack and capabilities available 4. 🎯 The project goals and objectives 5. 👤 The user's stated preferences and requirements 6. 🤖 The agent's capabilities and current state 7. 🔍 Project-specific patterns and best practices 8. ⚠️ Common issues and solutions for this context 9. 🔄 Recommended development workflow 10. 📊 The confidence level of available context Provide a comprehensive, context-aware response that: • Builds upon our conversation history • Leverages project-specific knowledge • Addresses the user's preferences • Suggests actionable next steps • References relevant technical details • Maintains conversation continuity === 🚀 END ENHANCED PROMPT === ``` ## 🧪 Testing ### **Run Tests** ```bash python test_prompt_generator.py ``` ### **Test Coverage** - ✅ All enhancement strategies - ✅ Convenience functions - ✅ Fallback behavior - ✅ Performance monitoring - ✅ Cache functionality - ✅ Error handling ## 📈 Performance Metrics ### **Monitoring** The system tracks: - Total prompts generated - Success/failure rates - Average generation time - Cache hit/miss rates - Context confidence scores ### **Optimization** - 5-minute cache TTL for context data - Intelligent cache size management - Performance tracking for optimization - Fallback strategies for reliability ## 🔧 Configuration ### **Cache Settings** ```python # Cache TTL (5 minutes) CACHE_TTL_SECONDS = 300 # Maximum cache size MAX_CACHE_SIZE = 100 # Cache cleanup threshold CACHE_CLEANUP_THRESHOLD = 20 ``` ### **Enhancement Strategies** ```python ENHANCEMENT_STRATEGIES = { 'comprehensive': _generate_comprehensive_prompt, 'technical': _generate_technical_prompt, 'conversation': _generate_conversation_prompt, 'smart': _generate_smart_prompt, 'minimal': _generate_minimal_prompt } ``` ## 🚀 Benefits ### **For Developers** - **Maintainability**: Single file to update prompt logic - **Consistency**: Uniform prompt structure across all strategies - **Debugging**: Centralized error handling and logging - **Testing**: Easy to test all prompt generation logic ### **For Users** - **Better Context**: More informative and structured prompts - **Faster Responses**: Intelligent caching reduces generation time - **Reliability**: Fallback strategies ensure system availability - **Flexibility**: Multiple enhancement strategies for different needs ### **For System** - **Performance**: Optimized caching and monitoring - **Scalability**: Easy to add new enhancement strategies - **Monitoring**: Comprehensive performance metrics - **Stability**: Robust error handling and fallbacks ## 🔮 Future Enhancements ### **Planned Features** - Machine learning-based context optimization - Dynamic strategy selection based on user patterns - Integration with external knowledge bases - Real-time context adaptation ### **Extensibility** - Plugin system for custom enhancement strategies - Configurable prompt templates - Multi-language support - Advanced caching strategies ## 📚 API Reference ### **PromptGenerator Class** #### **Methods** - `generate_enhanced_prompt(user_message, context_type, force_refresh)` - `get_stats()` - `clear_cache()` - `get_available_strategies()` #### **Properties** - `enhancement_stats`: Performance metrics - `context_cache`: Cached prompt results - `enhancement_strategies`: Available strategies ### **Convenience Functions** - `generate_comprehensive_prompt(user_message)` - `generate_technical_prompt(user_message)` - `generate_conversation_prompt(user_message)` - `generate_smart_prompt(user_message)` - `generate_minimal_prompt(user_message)` ## 🎉 Conclusion The Centralized Prompt Generator System represents a significant improvement in the MCP Conversation Intelligence System: - **🎯 Unified**: All prompt generation logic in one place - **🚀 Enhanced**: More informative and structured prompts - **🔧 Maintainable**: Easy to update and extend - **📊 Monitored**: Performance tracking and optimization - **🔄 Reliable**: Robust fallback strategies This system provides a solid foundation for future enhancements while maintaining backward compatibility and improving the overall user experience.