mcp-optimizer 0.3.7

MCP server for mathematical optimization problems using PuLP and OR-Tools

MCP Optimizer

🚀 Mathematical Optimization MCP Server with PuLP and OR-Tools support

🚀 Quick Start

Integration with LLM Clients

Claude Desktop Integration

Option 1: Using uvx (Recommended)

1. Install Claude Desktop from claude.ai
2. Open Claude Desktop → Settings → Developer → Edit Config
3. Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-optimizer": {
      "command": "uvx",
      "args": ["mcp-optimizer"]
    }
  }
}

4. Restart Claude Desktop and look for the 🔨 tools icon

Option 2: Using pip

pip install mcp-optimizer

Then add to your Claude Desktop config:

{
  "mcpServers": {
    "mcp-optimizer": {
      "command": "mcp-optimizer"
    }
  }
}

Option 3: Using Docker

Method A: Docker with stdio (Recommended)

docker pull ghcr.io/dmitryanchikov/mcp-optimizer:latest

Then add to your Claude Desktop config:

{
  "mcpServers": {
    "mcp-optimizer": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "ghcr.io/dmitryanchikov/mcp-optimizer:latest",
        "python", "main.py"
      ]
    }
  }
}

Method B: Docker as HTTP server (for advanced users)

docker run -d -p 8000:8000 ghcr.io/dmitryanchikov/mcp-optimizer:latest

Then use HTTP client to connect to http://localhost:8000 (requires additional MCP HTTP client setup)

Cursor Integration

1. Install the MCP extension in Cursor
2. Add mcp-optimizer to your workspace settings:

{
  "mcp.servers": {
    "mcp-optimizer": {
      "command": "uvx",
      "args": ["mcp-optimizer"]
    }
  }
}

Other LLM Clients

For other MCP-compatible clients (Continue, Cody, etc.), use similar configuration patterns with the appropriate command for your installation method.

Advanced Installation Options

Local Development

# Clone the repository
git clone https://github.com/dmitryanchikov/mcp-optimizer.git
cd mcp-optimizer

# Install dependencies with uv
uv sync --extra dev

# Run the server
uv run python main.py

Docker with Custom Configuration

# Build locally with optimization
git clone https://github.com/dmitryanchikov/mcp-optimizer.git
cd mcp-optimizer
docker build -t mcp-optimizer:optimized .
docker run -p 8000:8000 mcp-optimizer:optimized

# Check optimized image size (398MB vs 1.03GB original - 61% reduction!)
docker images mcp-optimizer:optimized

# Test the optimized image
./scripts/test_docker_optimization.sh

Standalone Server Commands

# Run directly with uvx (no installation needed)
uvx mcp-optimizer

# Or run specific commands
uvx mcp-optimizer --help

# With pip installation
mcp-optimizer

# Or run with Python module (use main.py for stdio mode)
python main.py

🎯 Features

Supported Optimization Problem Types:

• Linear Programming - Maximize/minimize linear objective functions
• Assignment Problems - Optimal resource allocation using Hungarian algorithm
• Transportation Problems - Logistics and supply chain optimization
• Knapsack Problems - Optimal item selection (0-1, bounded, unbounded)
• Routing Problems - TSP and VRP with time windows
• Scheduling Problems - Job and shift scheduling
• Integer Programming - Discrete optimization problems
• Financial Optimization - Portfolio optimization and risk management
• Production Planning - Multi-period production planning

Testing

# Run simple functionality tests
uv run python simple_test.py

# Run comprehensive integration tests
uv run python tests/test_integration/comprehensive_test.py

# Run all unit tests
uv run pytest tests/ -v

# Run with coverage
uv run pytest tests/ --cov=src/mcp_optimizer --cov-report=html

📊 Usage Examples

Linear Programming

from mcp_optimizer.tools.linear_programming import solve_linear_program

# Maximize 3x + 2y subject to:
# x + y <= 4
# 2x + y <= 6
# x, y >= 0

objective = {"sense": "maximize", "coefficients": {"x": 3, "y": 2}}
variables = {
    "x": {"type": "continuous", "lower": 0},
    "y": {"type": "continuous", "lower": 0}
}
constraints = [
    {"expression": {"x": 1, "y": 1}, "operator": "<=", "rhs": 4},
    {"expression": {"x": 2, "y": 1}, "operator": "<=", "rhs": 6}
]

result = solve_linear_program(objective, variables, constraints)
# Result: x=2.0, y=2.0, objective=10.0

Assignment Problem

from mcp_optimizer.tools.assignment import solve_assignment_problem

workers = ["Alice", "Bob", "Charlie"]
tasks = ["Task1", "Task2", "Task3"]
costs = [
    [4, 1, 3],  # Alice's costs for each task
    [2, 0, 5],  # Bob's costs for each task
    [3, 2, 2]   # Charlie's costs for each task
]

result = solve_assignment_problem(workers, tasks, costs)
# Result: Total cost = 5.0 with optimal assignments

Knapsack Problem

from mcp_optimizer.tools.knapsack import solve_knapsack_problem

items = [
    {"name": "Item1", "weight": 10, "value": 60},
    {"name": "Item2", "weight": 20, "value": 100},
    {"name": "Item3", "weight": 30, "value": 120}
]

result = solve_knapsack_problem(items, capacity=50)
# Result: Total value = 220.0 with optimal item selection

Portfolio Optimization

from mcp_optimizer.tools.financial import optimize_portfolio

assets = [
    {"name": "Stock A", "expected_return": 0.12, "risk": 0.18},
    {"name": "Stock B", "expected_return": 0.10, "risk": 0.15},
    {"name": "Bond C", "expected_return": 0.06, "risk": 0.08}
]

result = optimize_portfolio(
    assets=assets,
    objective="minimize_risk",
    budget=10000,
    risk_tolerance=0.15
)
# Result: Optimal portfolio allocation with minimized risk

🏗️ Architecture

mcp-optimizer/
├── src/mcp_optimizer/
│   ├── tools/           # 9 categories of optimization tools
│   │   ├── linear_programming.py
│   │   ├── assignment.py
│   │   ├── knapsack.py
│   │   ├── routing.py
│   │   ├── scheduling.py
│   │   ├── financial.py
│   │   └── production.py
│   ├── solvers/         # PuLP and OR-Tools integration
│   │   ├── pulp_solver.py
│   │   └── ortools_solver.py
│   ├── schemas/         # Pydantic validation schemas
│   ├── utils/           # Utility functions
│   ├── config.py        # Configuration
│   └── mcp_server.py    # Main MCP server
├── tests/               # Comprehensive test suite
├── docs/                # Documentation
├── k8s/                 # Kubernetes deployment
├── monitoring/          # Grafana/Prometheus setup
└── main.py             # Entry point

🧪 Test Results

✅ Comprehensive Test Suite

🧪 Starting Comprehensive MCP Optimizer Tests
==================================================
✅ Server Health PASSED
✅ Linear Programming PASSED
✅ Assignment Problems PASSED  
✅ Knapsack Problems PASSED
✅ Routing Problems PASSED
✅ Scheduling Problems PASSED
✅ Financial Optimization PASSED
✅ Production Planning PASSED
✅ Performance Test PASSED

📊 Test Results: 9 passed, 0 failed
🎉 All tests passed! MCP Optimizer is ready for production!

✅ Unit Tests

• 66 tests passed, 9 skipped
• Execution time: 0.45 seconds
• All core components functional

📈 Performance Metrics

• Linear Programming: ~0.01s
• Assignment Problems: ~0.01s
• Knapsack Problems: ~0.01s
• Complex test suite: 0.02s for 3 optimization problems
• Overall performance: 🚀 Excellent!

🔧 Technical Details

Core Solvers

• OR-Tools: For assignment, transportation, knapsack problems
• PuLP: For linear/integer programming
• FastMCP: For MCP server integration

Supported Solvers

• CBC, GLPK, GUROBI, CPLEX (via PuLP)
• SCIP, CP-SAT (via OR-Tools)

Key Features

• ✅ Full MCP protocol integration
• ✅ Comprehensive input validation
• ✅ Robust error handling
• ✅ High-performance optimization
• ✅ Production-ready architecture
• ✅ Extensive test coverage
• ✅ Docker and Kubernetes support

📋 Requirements

• Python 3.11+
• uv (for dependency management)
• OR-Tools (automatically installed)
• PuLP (automatically installed)

🚀 Production Deployment

Docker

# Build image
docker build -t mcp-optimizer .

# Run container
docker run -p 8000:8000 mcp-optimizer

Kubernetes

# Deploy to Kubernetes
kubectl apply -f k8s/

Monitoring

# Start monitoring stack
docker-compose up -d

🎯 Project Status

✅ PRODUCTION READY 🚀

• All core optimization tools implemented and tested
• MCP server fully functional
• Comprehensive test coverage (66 unit tests + 9 integration tests)
• OR-Tools integration confirmed working
• Performance optimized (< 30s for complex test suites)
• Ready for production deployment

📖 Usage Examples

The examples/ directory contains practical examples and prompts for using MCP Optimizer with Large Language Models (LLMs):

Available Examples

• 📊 Linear Programming (RU | EN)
  • Production optimization, diet planning, transportation, blending problems
• 👥 Assignment Problems (RU | EN)
  • Employee-project assignment, machine-order allocation, task distribution
• 💰 Portfolio Optimization (RU | EN)
  • Investment portfolios, retirement planning, risk management

How to Use Examples

1. For LLM Integration: Copy the prompt text and provide it to your LLM with MCP Optimizer access
2. For Direct API Usage: Use the provided API structures directly with MCP Optimizer functions
3. For Learning: Understand different optimization problem types and formulations

Each example includes:

• Problem descriptions and real-world scenarios
• Ready-to-use prompts for LLMs
• Technical API structures
• Common activation phrases
• Practical applications

🔄 Recent Updates

Latest Release Features:

1. Function Exports - Added exportable functions to all tool modules:

   • solve_linear_program() in linear_programming.py
   • solve_assignment_problem() in assignment.py
   • solve_knapsack_problem() in knapsack.py
   • optimize_portfolio() in financial.py
   • optimize_production() in production.py

2. Enhanced Testing - Updated comprehensive test suite with correct function signatures

3. OR-Tools Integration - Confirmed full functionality of all OR-Tools components

🚀 Fully Automated Release Process

New Simplified Git Flow (3 steps!)

The project uses a fully automated release process:

1. Create Release Branch

# For minor release (auto-increment)
uv run python scripts/release.py --type minor

# For specific version
uv run python scripts/release.py 0.2.0

# For hotfix
uv run python scripts/release.py --hotfix --type patch

# Preview changes
uv run python scripts/release.py --type minor --dry-run

2. Create PR to main

# Create PR: release/v0.3.0 → main
gh pr create --base main --head release/v0.3.0 --title "Release v0.3.0"

3. Merge PR - DONE! 🎉

After PR merge, automatically happens:

• ✅ Create tag v0.3.0
• ✅ Publish to PyPI
• ✅ Publish Docker images
• ✅ Create GitHub Release
• ✅ Merge main back to develop
• ✅ Cleanup release branch

NO NEED to run finalize_release.py manually anymore!

🔒 Secure Detection: Uses hybrid approach combining GitHub branch protection with automated release detection. See Release Process for details.

Automated Release Pipeline

The CI/CD pipeline automatically handles:

• ✅ Release Candidates: Built from release/* branches
• ✅ Production Releases: Triggered by version tags on main
• ✅ PyPI Publishing: Automatic on tag creation
• ✅ Docker Images: Multi-architecture builds
• ✅ GitHub Releases: With artifacts and release notes

CI/CD Pipeline

The GitHub Actions workflow automatically:

• ✅ Runs tests on Python 3.11 and 3.12
• ✅ Performs security scanning
• ✅ Builds and pushes Docker images
• ✅ Publishes to PyPI on tag creation
• ✅ Creates GitHub releases

Requirements for PyPI Publication

• Set PYPI_API_TOKEN secret in GitHub repository
• Ensure all tests pass
• Follow semantic versioning

🛠️ Development Tools

Debug Tools

Use the debug script to inspect MCP server structure:

# Run debug tools to check server structure
uv run python scripts/debug_tools.py

# This will show:
# - Available MCP tools
# - Tool types and attributes
# - Server configuration

Comprehensive Testing

Run the full integration test suite:

# Run comprehensive tests
uv run python tests/test_integration/comprehensive_test.py

# This tests:
# - All optimization tools (9 categories)
# - Server health and functionality
# - Performance benchmarks
# - End-to-end workflows

Docker Build Instructions

Image Details

• Base: Python 3.12 Slim (Debian-based)
• Size: ~649MB (optimized with multi-stage builds)
• Architecture: Multi-platform support (x86_64, ARM64)
• Security: Non-root user, minimal dependencies
• Performance: Optimized Python bytecode, cleaned build artifacts

Local Build Commands

# Standard build
docker build -t mcp-optimizer:latest .

# Build with development dependencies
docker build --build-arg ENV=development -t mcp-optimizer:dev .

# Build with cache mount for faster rebuilds
docker build --mount=type=cache,target=/build/.uv -t mcp-optimizer .

# Check image size
docker images mcp-optimizer

# Run container
docker run -p 8000:8000 mcp-optimizer:latest

# For development with volume mounting
docker run -p 8000:8000 -v $(pwd):/app mcp-optimizer:latest

# Test container functionality
docker run --rm mcp-optimizer:latest python -c "from mcp_optimizer.mcp_server import create_mcp_server; print('✅ MCP Optimizer works!')"

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Git Flow Policy

This project follows a standard Git Flow workflow:

• Feature branches → develop branch
• Release branches → main branch
• Hotfix branches → main and develop branches

📚 Documentation:

• Contributing Guide - Complete development workflow and Git Flow policy
• Release Process - How releases are created and automated
• Repository Setup - Complete setup guide including branch protection and security configuration

Development Setup

# Clone and setup
git clone https://github.com/dmitryanchikov/mcp-optimizer.git
cd mcp-optimizer

# Create feature branch from develop
git checkout develop
git checkout -b feature/your-feature-name

# Install dependencies
uv sync --extra dev

# Run tests
uv run pytest tests/ -v

# Run linting
uv run ruff check src/
uv run mypy src/

# Create PR to develop branch (not main!)

📄 License

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

🙏 Acknowledgments

• OR-Tools - Google's optimization tools
• PuLP - Linear programming in Python
• FastMCP - Fast MCP server implementation

📞 Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues
• 📖 Documentation: docs/

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