Supabase MCP Server

# Supabase MCP Server - Project Planning ## Overview This project aims to create a custom Model Context Protocol (MCP) server that integrates with a self-hosted Supabase instance. The server will serve as a comprehensive coding data aggregation platform, enabling AI assistants to interact with structured coding knowledge from various frameworks and technologies. ## Project Scope ### Primary Goals - **Universal Coding Data Hub**: Create a centralized repository for coding data from all major frameworks, languages, and development tools - **MCP Integration**: Implement a robust MCP server that provides standardized access to coding knowledge - **Self-Hosted Architecture**: Support self-hosted Supabase instances for data sovereignty and privacy - **Future-Proof Design**: Build extensible architecture to accommodate new frameworks and data sources - **AI-Powered Querying**: Enable intelligent code search, pattern recognition, and best practices retrieval ### Secondary Goals - **Real-time Updates**: Support real-time data synchronization as coding standards evolve - **Multi-Modal Data**: Handle various data types (code snippets, documentation, examples, benchmarks) - **Version Control Integration**: Track coding practice evolution over time - **Community Contributions**: Enable community-driven data curation and validation ## Technology Stack ### Core Technologies - **Protocol**: Model Context Protocol (MCP) v1.0+ - **Backend Language**: Python 3.11+ - **Database**: Self-hosted Supabase (PostgreSQL 15+) - **MCP SDK**: Official Python MCP SDK (`mcp` package) - **API Framework**: FastAPI or FastMCP for high-performance endpoints - **Database ORM**: SQLAlchemy 2.0+ or Supabase Python client ### Infrastructure & DevOps - **Containerization**: Docker & Docker Compose - **Database Hosting**: Self-hosted Supabase via Docker - **Environment Management**: Poetry or pip-tools - **Testing**: pytest with async support - **Logging**: structlog for structured logging - **Configuration**: Pydantic settings with environment variables ### Data Management - **Data Pipeline**: Custom ETL processes for framework data ingestion - **Search Engine**: PostgreSQL full-text search + pgvector for semantic search - **Caching**: Redis for query caching and session management - **Backup Strategy**: Automated PostgreSQL backups with retention policies ## Architecture Design ### High-Level Architecture ``` ┌─────────────────┐ ┌──────────────────┐ ┌───────────────────┐ │ AI Clients │◄──►│ MCP Server │◄──►│ Supabase DB │ │ (Claude, etc.) │ │ (Python) │ │ (Self-hosted) │ └─────────────────┘ └──────────────────┘ └───────────────────┘ │ │ ▼ ▼ ┌──────────────────┐ ┌───────────────────┐ │ Data Ingestion │ │ Vector Store │ │ Pipeline │ │ (pgvector) │ └──────────────────┘ └───────────────────┘ ``` ### Database Schema Design - **Frameworks Table**: Core framework metadata (name, version, category, etc.) - **Code_Patterns Table**: Reusable code patterns and templates - **Documentation Table**: Framework documentation and guides - **Examples Table**: Code examples with explanations - **Best_Practices Table**: Curated best practices and anti-patterns - **Dependencies Table**: Framework dependency relationships - **Updates_Log Table**: Track changes and version history ### MCP Server Components 1. **Tools**: Database query tools, code search, pattern matching 2. **Resources**: Framework documentation, code examples, best practices 3. **Prompts**: Predefined queries for common coding scenarios ## Data Sources & Integration Strategy ### Initial Framework Coverage - **Web Frameworks**: React, Vue, Angular, Svelte, Next.js, Nuxt.js - **Backend Frameworks**: Django, FastAPI, Express.js, Spring Boot, Rails - **Mobile Frameworks**: React Native, Flutter, Swift UI, Jetpack Compose - **AI/ML Frameworks**: PyTorch, TensorFlow, Hugging Face, LangChain - **DevOps Tools**: Docker, Kubernetes, Terraform, GitHub Actions ### Data Collection Methods 1. **Official Documentation Scraping**: Automated extraction from official docs 2. **GitHub Repository Analysis**: Code pattern extraction from popular repos 3. **Community Curation**: Expert-reviewed best practices and examples 4. **API Integration**: Direct integration with framework APIs where available 5. **Manual Curation**: High-quality, curated examples for complex scenarios ## Security & Privacy Considerations ### Data Security - **Self-Hosted Control**: All data remains within user's infrastructure - **Access Control**: Role-based access control (RBAC) for different user types - **API Security**: JWT-based authentication with rate limiting - **Data Encryption**: Encryption at rest and in transit ### Privacy Compliance - **No External Dependencies**: Minimize external API calls for data privacy - **Audit Logging**: Comprehensive audit trails for data access and modifications - **Data Anonymization**: Remove sensitive information from code examples ## Scalability & Performance ### Performance Targets - **Query Response Time**: <200ms for standard queries - **Concurrent Users**: Support 100+ concurrent MCP connections - **Data Volume**: Handle 1M+ code examples and patterns - **Search Performance**: Sub-second semantic search across entire knowledge base ### Scalability Strategy - **Horizontal Scaling**: Stateless MCP server design for easy scaling - **Database Optimization**: Advanced PostgreSQL indexing and partitioning - **Caching Strategy**: Multi-layer caching (Redis, application, CDN) - **Async Processing**: Background processing for data ingestion and updates ## Development Phases ### Phase 1: Foundation (Weeks 1-2) - MCP server skeleton with basic Supabase integration - Core database schema and migrations - Basic CRUD operations for frameworks and patterns ### Phase 2: Core Features (Weeks 3-4) - Implement all MCP tools and resources - Add full-text and semantic search capabilities - Build data ingestion pipeline for first 5 frameworks ### Phase 3: Enhancement (Weeks 5-6) - Advanced querying and filtering capabilities - Real-time data synchronization - Performance optimization and caching ### Phase 4: Production Ready (Weeks 7-8) - Comprehensive testing and documentation - Security hardening and monitoring - Deployment automation and CI/CD ## Success Metrics ### Technical Metrics - **API Response Time**: Average query response under 200ms - **System Uptime**: 99.9% availability target - **Data Freshness**: Framework data updated within 24 hours of upstream changes - **Test Coverage**: >95% code coverage with comprehensive integration tests ### User Experience Metrics - **Query Accuracy**: >90% relevant results for coding queries - **Framework Coverage**: Support for top 50 development frameworks - **User Adoption**: Successful integration with major AI development tools ## Risk Assessment & Mitigation ### Technical Risks - **MCP Protocol Evolution**: Risk of breaking changes in MCP specification - *Mitigation*: Use official SDK and maintain backward compatibility - **Database Performance**: Large-scale data querying performance degradation - *Mitigation*: Implement advanced indexing and query optimization - **Data Quality**: Inconsistent or outdated framework information - *Mitigation*: Implement automated validation and community review processes ### Operational Risks - **Maintenance Overhead**: Complex data pipeline maintenance requirements - *Mitigation*: Comprehensive automation and monitoring - **Community Adoption**: Low adoption due to existing solutions - *Mitigation*: Focus on unique value proposition and superior user experience ## Future Enhancements ### Advanced Features - **AI-Powered Code Generation**: Generate code snippets based on natural language - **Intelligent Code Review**: Automated code quality assessment and suggestions - **Learning Path Recommendations**: Personalized learning paths for different frameworks - **Integration Ecosystem**: Plugins for popular IDEs and development tools ### Community Features - **Contribution Platform**: Allow developers to contribute patterns and examples - **Rating System**: Community-driven quality assessment of code patterns - **Discussion Forums**: Platform for discussing best practices and patterns - **Expert Verification**: Expert review system for high-quality content curation ## Conclusion This project represents a significant step toward democratizing access to high-quality coding knowledge through standardized AI interfaces. By leveraging the power of MCP and self-hosted Supabase, we create a future-proof platform that can evolve with the rapidly changing development landscape while maintaining data sovereignty and privacy. The modular architecture ensures that the system can adapt to new frameworks, protocols, and AI capabilities as they emerge, making this a truly future-proof investment in developer productivity and AI-assisted development.