Provides persistent storage with vector indexing capabilities for the knowledge base, allowing for efficient storage and retrieval of structured content types.
Serves as the runtime environment for the MCP server, with specific compatibility for Node.js version 18 and above.
Utilizes OpenAI's embedding models for semantic search capabilities, enabling efficient retrieval of relevant content from the knowledge base.
Offers an alternative database option to MongoDB for persistent storage with vector indexing in the knowledge management system.
SDOF MCP - Structured Decision Optimization Framework
Next-generation knowledge management system with 5-phase optimization workflow
The Structured Decision Optimization Framework (SDOF) Knowledge Base is a Model Context Protocol (MCP) server that provides persistent memory and context management for AI systems through a structured 5-phase optimization workflow.
🚀 Quick Start
Prerequisites
- Node.js 18+
- OpenAI API Key (for embeddings)
- MCP-compatible client (Claude Desktop, etc.)
Installation
📖 Documentation
- Installation Guide - Complete setup instructions
- Migration Guide - Migration from ConPort
- API Documentation - MCP tool reference
- Setup Guide - Detailed configuration
✨ Features
🎯 5-Phase Optimization Workflow
- Phase 1: Exploration - Solution discovery and brainstorming
- Phase 2: Analysis - Detailed evaluation and optimization
- Phase 3: Implementation - Code development and testing
- Phase 4: Evaluation - Performance and quality assessment
- Phase 5: Integration - Learning consolidation and documentation
🧠 Advanced Knowledge Management
- Vector Embeddings: Semantic search with OpenAI embeddings
- Persistent Storage: MongoDB/SQLite with vector indexing
- Prompt Caching: Optimized for LLM efficiency
- Schema Validation: Structured content types
- Multi-Interface: Both MCP tools and HTTP API
🔧 Content Types
text
- General documentation and notescode
- Code implementations and examplesdecision
- Decision records and rationaleanalysis
- Analysis results and findingssolution
- Solution descriptions and designsevaluation
- Evaluation reports and metricsintegration
- Integration documentation and guides
🛠️ MCP Tools
Primary Tool: store_sdof_plan
Store structured knowledge with metadata:
Example Usage
🏗️ Architecture
🔧 Configuration
MCP Client Configuration
Add to your MCP client configuration:
Environment Variables
🧪 Testing
📊 Performance
Target metrics:
- Query Response: <500ms average
- Embedding Generation: <2s per request
- Vector Search: <100ms for similarity calculations
- Database Operations: <50ms for CRUD operations
🤝 Contributing
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature
- Make changes to TypeScript files in
src/
- Run tests:
npm test
- Build:
npm run build
- Commit changes:
git commit -m 'Add amazing feature'
- Push to branch:
git push origin feature/amazing-feature
- Open a Pull Request
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🆘 Support
- Documentation: Check the docs/ directory
- Issues: GitHub Issues
- Installation Help: See SDOF_INSTALLATION_GUIDE.md
🎉 Success Indicators
You know the system is working correctly when:
- ✅ No authentication errors in logs
- ✅
store_sdof_plan
tool responds successfully - ✅ Knowledge entries are stored and retrievable
- ✅ Query performance meets targets (<500ms)
- ✅ Test suite passes completely
Built with ❤️ for the AI community
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
A Model Context Protocol (MCP) server that provides persistent memory and context management for AI systems through a structured 5-phase optimization workflow.
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