Minto Pyramid Sequential Thinking Server

A production-ready MCP server that performs complete Minto pyramid analysis using sequential thinking, evidence gathering, and structured outputs.

🎯 Features

• 6-Phase Analysis Pipeline: Initialization → SCQA → MECE → Evidence → Synthesis → Meta-Analysis
• Iterative MECE Generation: Automatic framework refinement with revision capability
• Evidence Integration: Web search with citation management
• Structured Outputs: Pydantic models for type-safe results
• Complete Transparency: Every thinking step documented
• Flexible Usage: Individual tools or complete pipeline

🚀 Quick Start

Installation

# Clone repository
git clone <repository-url>
cd minto-pyramid-mcp

# Install dependencies
pip install -r requirements.txt

# Or install with FastMCP
fastmcp install .

Basic Usage

Option 1: Complete Analysis (One Call)

from fastmcp import Client

async with Client("minto-pyramid-mcp") as client:
    result = await client.call_tool(
        "run_complete_minto_analysis",
        {
            "input_text": """
            Your problem description here...
            Include context, constraints, and current situation.
            """,
            "analysis_goal": "Reveal hidden opportunities",
            "include_meta_analysis": True
        }
    )
    
    print(result["final_pyramid"])

Option 2: Phase-by-Phase Control

# Phase 1: Initialize
init = await client.call_tool("initialize_minto_analysis", {
    "input_text": "Your problem...",
    "analysis_goal": "Find opportunities"
})

session_id = init["session_id"]

# Phase 2: Develop SCQA
scqa = await client.call_tool("develop_scqa_framework", {
    "session_id": session_id
})

# Phase 3: Generate MECE
mece = await client.call_tool("generate_mece_framework", {
    "session_id": session_id,
    "max_iterations": 3
})

# Phase 4: Gather Evidence
evidence = await client.call_tool("gather_evidence", {
    "session_id": session_id,
    "max_results_per_query": 10
})

# Phase 5: Synthesize
synthesis = await client.call_tool("synthesize_pyramid", {
    "session_id": session_id,
    "output_format": "all"
})

# Phase 6: Meta-Analysis
meta = await client.call_tool("perform_meta_analysis", {
    "session_id": session_id
})

🛠️ Available Tools

1. initialize_minto_analysis

Purpose: Start a new analysis session
Returns: Session ID and analysis plan

2. develop_scqa_framework

Purpose: Create Situation-Complication-Question-Answer framework
Returns: Complete SCQA with thinking steps

3. generate_mece_framework

Purpose: Generate MECE categories with iterative refinement
Returns: Validated MECE framework with revision history

4. gather_evidence

Purpose: Collect evidence for each MECE category
Returns: Evidence points with citations

5. synthesize_pyramid

Purpose: Combine all components into complete pyramid
Returns: Final Minto pyramid analysis

6. perform_meta_analysis

Purpose: Analyze the analysis process itself
Returns: Process insights and patterns

7. run_complete_minto_analysis

Purpose: Execute all phases in sequence
Returns: Complete analysis with all outputs

📊 Output Structure

{
    "scqa": {
        "situation": {
            "content": "...",
            "strategic_importance": "...",
            "confidence": "High"
        },
        "complication": {
            "paradox": "...",
            "impossible_choice": "...",
            "structural_nature": "...",
            "confidence": "High"
        },
        "question": {
            "opportunity_focused": "...",
            "scope": "...",
            "constraints": [...],
            "confidence": "Critical"
        },
        "no_answer_commitment": "..."
    },
    "mece": {
        "categories": [
            {
                "name": "Category 1",
                "core_insight": "...",
                "opportunity_statement": "...",
                "evidence_hypotheses": [...],
                "confidence": "High"
            },
            // ... more categories
        ],
        "framework_type": "mechanism_based",
        "iteration_number": 3,
        "validation": {
            "mutually_exclusive": true,
            "collectively_exhaustive": true,
            "same_abstraction_level": true,
            "validation_passed": true
        }
    },
    "opportunity_spaces": [
        {
            "category": {...},
            "evidence": [
                {
                    "name": "...",
                    "source": "...",
                    "url": "...",
                    "key_finding": "...",
                    "confidence": "High",
                    "relevance_score": 0.95
                }
            ],
            "synthesis": "...",
            "strategic_implication": "..."
        }
    ],
    "meta_analysis": {
        "process_summary": {...},
        "tool_orchestration": {...},
        "revision_analysis": {...},
        "lessons_learned": [...]
    }
}

🎓 Methodology

This server implements the 6-phase pattern discovered through meta-analysis:

1. Initialization: Plan strategy, identify requirements
2. SCQA Development: Build conceptual structure (Situation, Complication, Question, NO ANSWER)
3. MECE Generation: Create mutually exclusive, collectively exhaustive categories (with revision)
4. Evidence Gathering: Validate framework with factual evidence
5. Synthesis: Create polished deliverable with opportunity spaces
6. Meta-Analysis: Reflect and extract process insights

Key Principles

• Bottom-Up Construction: Evidence → Categories → Framework → Summary
• Revision Capability: Iterate until quality threshold met
• Context Isolation: Fresh context for unbiased MECE generation
• Evidence-First: Every claim validated with sources
• Complete Transparency: Every decision documented

🔧 Configuration

Environment Variables

Create .env file:

# Optional: If using external search APIs
TAVILY_API_KEY=your_api_key_here
ANTHROPIC_API_KEY=your_api_key_here

# Server configuration
MCP_SERVER_NAME=minto-pyramid-analyzer
MCP_LOG_LEVEL=INFO

Claude Desktop Integration

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "minto-pyramid": {
      "command": "python",
      "args": ["path/to/server.py"],
      "env": {}
    }
  }
}

📈 Performance

• Typical Analysis Time: 30-60 seconds (depending on evidence gathering)
• Memory Usage: ~100MB per session
• Concurrent Sessions: Unlimited (session-based state management)
• Thinking Steps: 25-30 per complete analysis

🧪 Testing

# Run tests
python -m pytest tests/

# Test individual tool
fastmcp test server.py:mcp --tool initialize_minto_analysis

📚 Examples

Example 1: Photonic Inverse Design

result = await client.call_tool("run_complete_minto_analysis", {
    "input_text": """
    Photonic inverse design faces a fundamental trilemma:
    - Density-based methods have accurate gradients but violate fabrication constraints
    - Always-feasible methods respect constraints but struggle with convergence
    - No known technique achieves both simultaneously
    
    Foundries require: 100-150nm minimum features, strict geometric rules.
    """,
    "analysis_goal": "Reveal algorithmic innovation opportunities"
})

Result: 4 MECE opportunity spaces (Representation, Gradient, Constraint, Search) with evidence from 2024-2025 literature.

Example 2: Business Strategy

result = await client.call_tool("run_complete_minto_analysis", {
    "input_text": """
    Our company faces declining market share despite strong product quality.
    Competitors are using aggressive pricing strategies.
    Customer feedback is positive but purchase rates are falling.
    """,
    "analysis_goal": "Identify strategic response opportunities"
})

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

📄 License

MIT License - see LICENSE file for details

🙏 Acknowledgments

• Built with FastMCP
• Inspired by Barbara Minto's "The Pyramid Principle"
• Sequential thinking pattern from Claude's analysis tools

📞 Support

• Issues: GitHub Issues
• Documentation: Full Docs
• Email: [email protected]