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.
๐ Quick Start
Prerequisites
Node.js 18+
OpenAI API Key (for embeddings)
MCP-compatible client (Claude Desktop, etc.)
Installation
๐ Documentation
Installation Guide - Complete setup instructions
Migration Guide - Migration from ConPort
API Documentation - MCP tool reference
Setup Guide - Detailed configuration
โจ Features
๐ฏ 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
๐ง 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
๐ง 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
๐ ๏ธ MCP Tools
Primary Tool: store_sdof_plan
Store structured knowledge with metadata:
Example Usage
๐๏ธ Architecture
๐ง Configuration
MCP Client Configuration
Add to your MCP client configuration:
Environment Variables
๐งช Testing
๐ Performance
Target metrics:
Query Response: <500ms average
Embedding Generation: <2s per request
Vector Search: <100ms for similarity calculations
Database Operations: <50ms for CRUD operations
๐ค 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
๐ License
This project is licensed under the MIT License - see the LICENSE file for details.
๐ Support
Documentation: Check the docs/ directory
Issues: GitHub Issues
Installation Help: See SDOF_INSTALLATION_GUIDE.md
๐ Success Indicators
You know the system is working correctly when:
โ No authentication errors in logs
โ
store_sdof_plan
tool responds successfullyโ Knowledge entries are stored and retrievable
โ Query performance meets targets (<500ms)
โ Test suite passes completely
Built with โค๏ธ 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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