Skip to main content
Glama

SDOF Knowledge Base

by tgf-between-your-legs
GitHub
TypeScript
MIT License
OverviewInspectNewSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

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

✨ 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 notes
  • code - Code implementations and examples
  • decision - Decision records and rationale
  • analysis - Analysis results and findings
  • solution - Solution descriptions and designs
  • evaluation - Evaluation reports and metrics
  • integration - 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

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/amazing-feature
  3. Make changes to TypeScript files in src/
  4. Run tests: npm test
  5. Build: npm run build
  6. Commit changes: git commit -m 'Add amazing feature'
  7. Push to branch: git push origin feature/amazing-feature
  8. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

🎉 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

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

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.

  1. 🚀 Quick Start
    1. Prerequisites
    2. Installation
  2. 📖 Documentation
    1. ✨ Features
      1. 🎯 5-Phase Optimization Workflow
      2. 🧠 Advanced Knowledge Management
      3. 🔧 Content Types
    2. 🛠️ MCP Tools
      1. Primary Tool: store_sdof_plan
      2. Example Usage
    3. 🏗️ Architecture
      1. 🔧 Configuration
        1. MCP Client Configuration
        2. Environment Variables
      2. 🧪 Testing
        1. 📊 Performance
          1. 🤝 Contributing
            1. 📄 License
              1. 🆘 Support
                1. 🎉 Success Indicators

                  Related MCP Servers

                  • MCP Agents Server

                    MCPAgents
                    -
                    security
                    F
                    license
                    -
                    quality
                    Facilitates interaction and context sharing between AI models using the standardized Model Context Protocol (MCP) with features like interoperability, scalability, security, and flexibility across diverse AI systems.
                    Last updated -
                    1
                    Python

                  • TxtAi Memory Vector Server

                    rmtech1
                    -
                    security
                    F
                    license
                    -
                    quality
                    Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities. You can use Claude and Cline AI Also
                    Last updated -
                    4
                    Python
                    • Apple

                  • MCP Local File Reader

                    sworddut
                    -
                    security
                    A
                    license
                    -
                    quality
                    A Model Context Protocol (MCP) server that allows AI models to safely access and interact with local file systems, enabling reading file contents, listing directories, and retrieving file metadata.
                    Last updated -
                    47
                    1
                    JavaScript
                    MIT License
                    • Linux

                  • PAELLADOC

                    jlcases
                    -
                    security
                    A
                    license
                    -
                    quality
                    A Model Context Protocol (MCP) server that implements AI-First Development framework principles, allowing LLMs to interact with context-first documentation tools and workflows for preserving knowledge and intent alongside code.
                    Last updated -
                    321
                    Python
                    AGPL 3.0

                  View all related MCP servers

                  Appeared in Searches

                  New MCP Servers

                  MCP directory API

                  We provide all the information about MCP servers via our MCP API.

                  curl -X GET 'https://glama.ai/api/mcp/v1/servers/tgf-between-your-legs/sdof-mcp'

                  If you have feedback or need assistance with the MCP directory API, please join our Discord server