dcisionai-mcp-server 3.0.4

DcisionAI MCP Server - AI-Powered Optimization for Cursor, Claude Desktop & VS Code

DcisionAI MCP Server

AI-Powered Optimization for Cursor, Claude Desktop & VS Code

Solve complex optimization problems directly in your IDE using natural language. Get mathematically-verified solutions with 90%+ trust scores in seconds.

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🚀 Quick Start

Installation (Zero Configuration!)

# That's it! No installation needed with uvx

Configure Your IDE

For Cursor or Claude Desktop:

Add to your MCP config file (~/Library/Application\ Support/Claude/claude_desktop_config.json on Mac):

{
  "mcpServers": {
    "dcisionai": {
      "command": "uvx",
      "args": ["dcisionai-mcp-server@latest"],
      "env": {
        "OPENAI_API_KEY": "${OPENAI_API_KEY}",
        "ANTHROPIC_API_KEY": "${ANTHROPIC_API_KEY}"
      },
      "autoApprove": ["dcisionai_solve"]
    }
  }
}

For VS Code: Add to your .vscode/settings.json:

{
  "mcp.servers": {
    "dcisionai": {
      "command": "uvx",
      "args": ["dcisionai-mcp-server@latest"],
      "env": {
        "OPENAI_API_KEY": "your-openai-key",
        "ANTHROPIC_API_KEY": "your-anthropic-key"
      }
    }
  }
}

Use It!

In Cursor or Claude Desktop, just ask:

"Use DcisionAI to optimize my $500K portfolio concentrated in tech stocks"

"Use DcisionAI to optimize delivery routes for 20 customers"

"Use DcisionAI to optimize employee scheduling for 30 workers across 50 shifts"

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✨ What Can It Do?

📊 Finance

• Portfolio rebalancing with risk constraints
• Trading schedule optimization
• Asset allocation with concentration limits
• Private equity exit timing

🏪 Retail

• Store layout optimization (shelf space allocation)
• Promotion scheduling with budget constraints
• Inventory placement optimization

🚚 Logistics

• Vehicle routing (VRP) with time windows
• Delivery route optimization
• Fleet allocation

👥 Workforce

• Employee scheduling with skill requirements
• Shift rostering with labor rules
• Resource allocation

🏭 Manufacturing

• Job shop scheduling
• Maintenance scheduling
• Production planning

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🎯 Why DcisionAI?

1. Natural Language → Optimized Solution

You: "Optimize 20 products across 5 shelves to maximize revenue"
   ⬇️
DcisionAI: Automatically classifies, extracts data, builds model, solves
   ⬇️
Result: Complete solution with 90%+ trust score in 15 seconds

2. Mathematical Proof

Every solution includes:

• ✅ Constraint Verification
• ✅ Monte Carlo Simulation (1000 trials)
• ✅ Optimality Certificate
• ✅ Sensitivity Analysis
• ✅ Benchmark Comparison
• ✅ Cross-Validation (HiGHS vs DAME)

3. Business-Friendly

• LLM-generated implementation steps
• Risk analysis & assumptions
• "What-if" scenarios
• Plain English explanations

4. Dual-Solver Validation

• DAME (DcisionAI Micro-differential Evolutionary Algorithm) - Works for ANY problem
• HiGHS - Provably optimal for LP/MIP
• Parallel execution → Higher trust scores

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📈 Example Output

{
  "status": "success",
  "industry": "RETAIL",
  "domain": "Store Layout Optimization",
  
  # Solution
  "objective_value": 0.427,
  "solution": {...},
  
  # Trust & Validation
  "trust_score": 0.92,  # 92% confidence!
  "certification": "VERIFIED",
  "mathematical_proof": {
    "constraint_verification": {"status": "verified", "confidence": 1.0},
    "monte_carlo_simulation": {"status": "verified", "confidence": 0.999},
    "optimality_certificate": {"status": "verified", "gap": 0.047},
    "sensitivity_analysis": {"status": "verified", "confidence": 1.0},
    "benchmark_comparison": {"status": "verified", "improvement": 42.3},
    "cross_validation": {"status": "verified", "agreement": 0.98}
  },
  
  # Business Insights
  "business_interpretation": {
    "summary": "Systematically optimized product placement across 5 shelves...",
    "key_decisions": {...},
    "implementation_steps": [...],
    "risks_and_assumptions": [...],
    "what_if_scenarios": [...]
  }
}

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🔧 Advanced Usage

Validation Modes

{
  "validation_mode": "auto"      // Smart routing (default)
  "validation_mode": "parallel"  // Both HiGHS + DAME (max trust)
  "validation_mode": "fast"      // Fastest solver only
  "validation_mode": "exact"     // HiGHS only (LP/MIP optimal)
  "validation_mode": "heuristic" // DAME only (any problem)
}

Environment Variables

Variable	Required	Description
OPENAI_API_KEY	Yes	OpenAI API key for LLM
ANTHROPIC_API_KEY	Yes	Anthropic API key for Claude
POLYGON_API_KEY	No	For real-time market data (finance domains)
ALPHA_VANTAGE_API_KEY	No	For economic/commodity data (finance domains)

Note: DcisionAI uses internal infrastructure (Supabase) for domain configurations. No additional setup needed!

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📚 Technical Details

DAME Algorithm

• DcisionAI Micro-differential Evolutionary Algorithm
• Proprietary heuristic solver
• Handles ANY optimization problem
• 0.1-3% optimality gap in 0.5-5 seconds

HiGHS Integration

• Open-source LP/MIP solver
• Provably optimal solutions
• Parallel validation with DAME
• Used for cross-validation proofs

Trust Scoring

Weighted average of 6 proofs:

• Constraint Verification: 25%
• Monte Carlo Simulation: 20%
• Optimality Certificate: 15%
• Sensitivity Analysis: 15%
• Benchmark Comparison: 10%
• Cross-Validation: 15%

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🤝 Contributing

We welcome contributions! See our GitHub repository for:

• Bug reports
• Feature requests
• Pull requests
• Documentation improvements

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📄 License

MIT License - See LICENSE for details.

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🔗 Links

• Homepage: dcisionai.com
• GitHub: github.com/ameydhavle/dcisionai-mcp-platform
• Issues: GitHub Issues
• Research Paper: DcisionAI Technical Paper

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💡 Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

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Made with ❤️ by the DcisionAI Team