MCP Personal Assistant

# MCP Personal Assistant - Claude Development Notes ## Current Implementation Analysis This project implements a comprehensive MCP (Model Context Protocol) server for personal productivity management with features including status tracking, project management, todos, calendar functionality, and document storage. ## Identified Limitations & Cloud Architecture Upgrade Plan ### Current Implementation Drawbacks #### 1. **Scalability Issues** - **Token Limit Problem**: Dashboard returns ALL active projects, todos, events - can exceed MCP token limits - **Memory Overhead**: Everything loaded in memory for each request - **Single User Design**: No multi-tenancy support - **Local Storage**: SQLite/TinyDB limits concurrent access and cloud deployment #### 2. **Data Retrieval Inefficiency** - **No Intelligent Filtering**: Returns complete datasets instead of relevant subsets - **No Semantic Search**: Basic SQL queries can't understand context/intent - **Poor Query Optimization**: No caching or query optimization strategies - **Linear Growth**: Response size grows linearly with data volume #### 3. **Cloud Deployment Limitations** - **Stateful Design**: Local file storage prevents horizontal scaling - **No Authentication**: No user isolation or security - **No Multi-tenancy**: Single database per deployment - **Limited Transport**: Only local MCP transport, no HTTP support ## Proposed Cloud-Native Architecture ### 1. **HTTP MCP Server with Multi-tenancy** ``` ┌─────────────────────────────────────────────────┐ │ Load Balancer │ ├─────────────────────────────────────────────────┤ │ MCP HTTP Server (FastAPI) │ │ ┌─────────────┐ ┌─────────────┐ ┌──────────────┐│ │ │ Auth Layer │ │ Rate Limiter│ │ Session Mgmt ││ │ └─────────────┘ └─────────────┘ └──────────────┘│ └─────────────────────────────────────────────────┘ │ ┌─────────────────────┼─────────────────────┐ │ │ │ ┌───▼────┐ ┌───────▼────────┐ ┌─────▼─────┐ │Vector │ │ PostgreSQL │ │ Redis │ │Database│ │ (pgvector) │ │ Cache │ │(pgvector)│ │ │ │ │ └────────┘ └────────────────┘ └───────────┘ ``` ### 2. **Hybrid Storage Architecture** - **PostgreSQL + pgvector**: Structured data + vector embeddings - **Redis**: Session management, rate limiting, caching - **S3/CloudFlare R2**: Document storage with CDN - **Vector Search**: Semantic search for projects, todos, documents ### 3. **Intelligent Data Retrieval System** #### **Smart Dashboard with RAG** ```python # Instead of returning ALL projects: async def get_intelligent_dashboard(user_query: str, user_id: str): # 1. Generate query embedding query_embedding = await get_embedding(user_query) # 2. Semantic search for relevant items relevant_projects = await vector_search( table="projects", embedding=query_embedding, user_id=user_id, limit=5 # Only top 5 most relevant ) # 3. Context-aware filtering filtered_todos = await contextual_todo_search( query_embedding, user_id, time_window="7d" ) return { "relevant_projects": relevant_projects, "priority_todos": filtered_todos[:3], "suggested_actions": await generate_suggestions(user_context) } ``` #### **Context-Aware Tool Responses** - **Semantic Search**: Use embeddings to find relevant projects/todos - **Intent Recognition**: Understand user's current focus area - **Smart Filtering**: Return only contextually relevant data - **Progressive Loading**: Load details on-demand ### 4. **Enhanced Data Models** #### **Vector-Enhanced Models** ```python class Project(BaseModel): # Existing fields... embedding: Optional[List[float]] = None # For semantic search context_tags: List[str] = [] # Auto-generated contextual tags relevance_score: Optional[float] = None # For ranking class UserContext(BaseModel): user_id: str current_focus: Optional[str] = None # What user is working on work_patterns: Dict[str, Any] = {} # Learning user preferences semantic_profile: Optional[List[float]] = None # User interest embedding ``` #### **Knowledge Base Integration** ```python class KnowledgeBase: """Hybrid structured + unstructured data""" async def store_with_context(self, item: Any, user_id: str): # Store in PostgreSQL await self.db.store(item) # Generate and store embedding embedding = await self.generate_embedding(item.content) await self.vector_store.store(item.id, embedding, user_id) # Extract and store structured metadata metadata = await self.extract_metadata(item) await self.metadata_store.store(item.id, metadata) ``` ### 5. **Cloud Deployment Strategy** #### **Platform Options** 1. **Cloudflare Workers + D1 + Vectorize** (Recommended) - Built-in OAuth handling - Global edge deployment - Vector search with Vectorize - Cost-effective 2. **AWS ECS + RDS + Aurora pgvector** - Enterprise-grade scalability - AWS Cognito for auth - Auto-scaling capabilities 3. **Google Cloud Run + Cloud SQL + Vertex AI** - Serverless scaling - Integrated ML capabilities - Vector embeddings API #### **Authentication & Authorization** ```python @app.middleware("http") async def authenticate_request(request: Request, call_next): # OAuth 2.0 with resource indicators (RFC 8707) token = await verify_oauth_token(request.headers.get("Authorization")) user_context = await get_user_context(token.user_id) request.state.user = user_context return await call_next(request) ``` ### 6. **Implementation Phases** #### **Phase 1: HTTP MCP Server Foundation** - Convert to FastAPI HTTP MCP server - Add OAuth authentication - Implement multi-tenancy - Deploy to cloud platform #### **Phase 2: Vector Search Integration** - Add pgvector to PostgreSQL - Implement embedding generation - Create semantic search endpoints - Add intelligent filtering #### **Phase 3: RAG Enhancement** - Build knowledge base system - Add context-aware responses - Implement learning user preferences - Create suggestion engine #### **Phase 4: Advanced Features** - Real-time collaboration - Advanced analytics - Plugin system for integrations - Mobile API support ### 7. **Performance Optimizations** #### **Token Usage Optimization** - **Pagination**: Return results in chunks - **Relevance Scoring**: Show only top-N most relevant items - **Lazy Loading**: Load details only when requested - **Caching**: Redis for frequently accessed data #### **Query Optimization** - **Index Strategy**: Proper indexing on vectors and metadata - **Connection Pooling**: Efficient database connections - **Query Batching**: Combine multiple queries - **CDN**: Cache static resources ### 8. **Success Metrics** - **Response Size**: < 4KB per dashboard request - **Response Time**: < 200ms for semantic search - **Scalability**: Support 1000+ concurrent users - **Accuracy**: >90% relevance in search results ## Development Commands ```bash # Setup make setup # Run server make run # Run tests make test # Clean make clean ``` ## Next Steps This architecture transforms the current local MCP server into a production-ready, cloud-native system that intelligently manages data retrieval, provides semantic search capabilities, and scales horizontally while maintaining the familiar MCP interface. Focus areas for implementation: 1. Start with HTTP MCP server conversion 2. Add PostgreSQL + pgvector integration 3. Implement semantic search for intelligent filtering 4. Deploy to chosen cloud platform with authentication ## Notes for Collaboration - Current implementation works well for single-user, local development - Cloud deployment requires significant architectural changes - Vector embeddings will dramatically improve data retrieval relevance - Multi-tenancy is essential for cloud deployment - Consider starting with Cloudflare Workers for rapid prototyping