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
# Clone the repository
git clone https://github.com/your-username/sdof-mcp.git
cd sdof-mcp
# Install dependencies
npm install
npm run build
# Configure environment
cp .env.example .env
# Edit .env with your OpenAI API key
# Start the server
npm start
📖 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:
{
plan_content: string; // Markdown content
metadata: {
planTitle: string; // Descriptive title
planType: ContentType; // Content type (text, code, decision, etc.)
tags?: string[]; // Categorization tags
phase?: string; // SDOF phase (1-5)
cache_hint?: boolean; // Mark for prompt caching
}
}
Example Usage
// Store a decision record
{
"server_name": "sdof_knowledge_base",
"tool_name": "store_sdof_plan",
"arguments": {
"plan_content": "# Database Selection\n\nChose MongoDB for vector storage due to...",
"metadata": {
"planTitle": "Database Architecture Decision",
"planType": "decision",
"tags": ["database", "architecture"],
"phase": "2",
"cache_hint": true
}
}
}
🏗️ Architecture
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ AI Clients │───▶│ SDOF Knowledge │───▶│ Database │
│ (Claude, etc.) │ │ Base MCP │ │ (MongoDB/ │
└─────────────────┘ │ Server │ │ SQLite) │
└──────────────────┘ └─────────────────┘
│
▼
┌──────────────────┐
│ HTTP API │
│ (Port 3000) │
└──────────────────┘
🔧 Configuration
MCP Client Configuration
Add to your MCP client configuration:
{
"mcpServers": {
"sdof_knowledge_base": {
"type": "stdio",
"command": "node",
"args": ["path/to/sdof-mcp/build/index.js"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key"
},
"alwaysAllow": ["store_sdof_plan"]
}
}
}
Environment Variables
# Required
OPENAI_API_KEY=sk-proj-your-openai-api-key
# Optional
EMBEDDING_MODEL=text-embedding-3-small
HTTP_PORT=3000
MONGODB_URI=mongodb://localhost:27017/sdof
🧪 Testing
# Run tests
npm test
# Run system validation
node build/test-unified-system.js
# Performance benchmarks
npm run test:performance
📊 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_plantool responds successfully - ✅ Knowledge entries are stored and retrievable
- ✅ Query performance meets targets (<500ms)
- ✅ Test suite passes completely
Built with ❤️ for the AI community