WFGY MCP Server - Augment Integration

Real $1M-level reasoning through MCP protocol integration with Augment

This repository contains an enhanced version of the WFGY (What's For Generating You) project with full Model Context Protocol (MCP) integration for Augment compatibility.

🚀 What This Provides

11 Production-Ready WFGY Tools

• ✅ wfgy_engine_run - Core WFGY reasoning with variance reduction
• ✅ wfgy_bbmc_process - BBMC Semantic Residue computation
• ✅ wfgy_bbpf_analyze - BBPF Workflow stability analysis
• ✅ wfgy_bbcr_recover - BBCR System state recovery
• ✅ wfgy_bbam_modulate - BBAM Attention modulation
• ✅ wfgy_problem_search - WFGY-enhanced problem search
• ✅ wfgy_problemmap_index - Content indexing with WFGY
• 🆕 wfgy_problemmap_search - Structured ProblemMap search
• 🆕 wfgy_problemmap_get - Specific problem retrieval (1-16)
• 🆕 wfgy_problemmap_diagnose - Symptom-based diagnosis
• ✅ wfgy_code_analyze - WFGY-enhanced code analysis

Enhanced ProblemMap Access

Access to 16 structured WFGY problems with specific fixes:

#	Problem	Category	Modules	Status
1	Hallucination & Chunk Drift	IN	BBCR, BBMC	✅ Stable
2	Interpretation Collapse	RE	BBCR	✅ Stable
3	Long Reasoning Chains	RE	BBMC, Tree	✅ Stable
4	Bluffing / Overconfidence	RE	BBCR, λ_observe	✅ Stable
5	Semantic ≠ Embedding	IN	BBMC, BBAM	✅ Stable
6	Logic Collapse & Recovery	RE	BBCR, BBPF	✅ Stable
7	Memory Breaks Across Sessions	ST	Tree, BBMC	✅ Stable
8	Multi-Agent Role Drift	ST	BBCR, BBPF	✅ Stable

🔧 Quick Start

1. Docker Deployment (Recommended)

# Clone your repository
git clone https://github.com/YOUR_USERNAME/WFGY-MCP.git
cd WFGY-MCP

# Start the MCP server
docker compose up -d

# Server runs on http://localhost:8052

2. Augment Integration

Add to your Augment MCP configuration:

{
  "mcpServers": {
    "wfgy": {
      "command": "docker",
      "args": ["exec", "wfgy-wfgy-1", "python", "-m", "wfgy_mcp.server"],
      "env": {}
    }
  }
}

3. Test the Tools

# Example: Diagnose AI problems
wfgy_problemmap_diagnose(symptoms="hallucination and wrong content")

# Example: Analyze workflow stability  
wfgy_bbpf_analyze(workflow="data input -> processing -> output")

# Example: Compute semantic residue
wfgy_bbmc_process(text="The universe is expanding", context="cosmology")

📊 Key Features

Real WFGY Processing

• ✅ Authentic variance reduction calculations
• ✅ Semantic residue computation with real BBMC
• ✅ Workflow stability analysis with BBPF
• ✅ System recovery protocols with BBCR
• ✅ Attention modulation with BBAM

Structured Knowledge Access

• 🎯 16 documented problems with specific fixes
• 🔍 Symptom-based diagnosis with pattern matching
• 📋 Category filtering (IN, RE, ST, OP)
• 🛠️ Module-specific solutions (BBMC, BBCR, BBPF, BBAM)

Production Ready

• 🐳 Docker deployment with docker-compose
• 🧪 Comprehensive test suite with contract tests
• 📝 Full MCP compliance for Augment integration
• 🔧 Tool naming compatibility (fixed dots → underscores)

🛠️ Development

Local Development

# Install dependencies
pip install -r requirements.txt

# Run tests
pytest tests/

# Start development server
python -m wfgy_mcp.server

Project Structure

wfgy_mcp/
├── server.py          # Main MCP server
├── schemas.py         # Pydantic schemas
├── wfgy_integration.py # WFGY SDK integration
└── problemmap.py      # ProblemMap data access

docker/
├── run_uvicorn.sh     # Docker startup script
└── Dockerfile.mcp     # MCP server container

tests/
├── contract/          # MCP contract tests
└── test_*.py         # Unit tests

📈 What's Enhanced

This repository builds on the original WFGY project with:

MCP Integration

• ✅ Full Model Context Protocol implementation
• ✅ Augment-compatible tool naming
• ✅ JSON-RPC 2.0 compliance
• ✅ Proper error handling and validation

Enhanced ProblemMap

• 🆕 Structured access to 16 core problems
• 🆕 Symptom-based diagnostic tools
• 🆕 Category and module filtering
• 🆕 Real-time problem recommendations

Production Deployment

• 🐳 Docker containerization
• 🔧 Environment configuration
• 📊 Health checks and monitoring
• 🧪 Automated testing pipeline

🎯 Use Cases

• AI Debugging: Diagnose and fix AI reasoning problems
• Semantic Analysis: Compute semantic residue and variance
• Workflow Optimization: Analyze process stability
• Code Quality: WFGY-enhanced code analysis
• Problem Solving: Access structured AI problem solutions

📄 License

Based on the original WFGY project. Enhanced with MCP integration.

🙏 Credits

• Original WFGY: onestardao/WFGY
• MCP Integration: Enhanced for Augment compatibility
• Enhanced ProblemMap: Structured access to WFGY knowledge base

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Ready to unlock $1M-level AI reasoning in Augment! 🚀