mcp-optimizer

dmitryanchikov/mcp-optimizer

3.2

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MCP Optimizer is a mathematical optimization server that supports PuLP and OR-Tools, designed for cross-platform deployment and integration with LLM clients.

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MCP Optimizer

๐Ÿš€ Mathematical Optimization MCP Server with PuLP and OR-Tools support

Tests Coverage Python

๐Ÿ“– Quick Links: ๐Ÿš€ Quick Start | ๐Ÿ”ง macOS Troubleshooting | ๐Ÿ“Š Examples | ๐ŸŽฏ Features

๐Ÿš€ Quick Start

Recommended Installation Methods (by Priority)

1. ๐Ÿณ Docker (Recommended) - Cross-platform

Most stable method with full functionality

# Run with STDIO transport (for MCP clients)
docker run --rm -i ghcr.io/dmitryanchikov/mcp-optimizer:latest

# Run with SSE transport (for remote clients)
docker run -d -p 8000:8000 -e TRANSPORT_MODE=sse \
  ghcr.io/dmitryanchikov/mcp-optimizer:latest

# Check SSE endpoint
curl -i http://localhost:8000/sse

2. ๐Ÿ“ฆ pip + venv - Cross-platform

Standard approach

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/macOS
# or .venv\Scripts\activate  # Windows

# Install mcp-optimizer
pip install mcp-optimizer

# For SSE issues, use stable dependency versions:
# pip install "mcp-optimizer[stable]"

# Run (STDIO mode recommended)
mcp-optimizer --transport stdio

3. ๐Ÿš€ uvx - Linux/Windows (full), macOS (partially)

# Linux/Windows - works out of the box
uvx mcp-optimizer

# macOS - requires Python 3.12
uvx --python python3.12 mcp-optimizer

# STDIO mode recommended
uvx mcp-optimizer --transport stdio

macOS users: If you encounter OR-Tools related errors, see ๐Ÿ”ง macOS uvx Troubleshooting section for automated fix scripts.

๐ŸŽ macOS Specifics

OR-Tools support:

  • uvx: PuLP only (limited functionality)
  • pip: full OR-Tools support
  • Docker: full OR-Tools support

For full OR-Tools support via pip:

# Install OR-Tools via Homebrew
brew install or-tools

# Then install mcp-optimizer
pip install "mcp-optimizer[stable]"

Transport Mode Recommendations

Installation MethodRecommended TransportWhy
DockerSSEFull stability
pip + venvSTDIOAvoids dependency issues with newer versions
uvxSTDIOMaximum compatibility

Integration with LLM Clients

Claude Desktop Integration

Option 1: Using Docker (Recommended)

  1. Install Claude Desktop from claude.ai
  2. Pull the Docker image:
docker pull ghcr.io/dmitryanchikov/mcp-optimizer:latest
  1. Add to your claude_desktop_config.json:
{
  "mcpServers": {
    "mcp-optimizer": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "ghcr.io/dmitryanchikov/mcp-optimizer:latest",
        "python", "main.py"
      ]
    }
  }
}
  1. Restart Claude Desktop and look for the ๐Ÿ”จ tools icon

Option 2: Using pip + venv

# Create virtual environment and install
python -m venv .venv
source .venv/bin/activate  # Linux/macOS
pip install mcp-optimizer

Then add to your Claude Desktop config:

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

Option 3: Using uvx Add to your claude_desktop_config.json:

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

Note: On macOS, uvx provides limited functionality (PuLP solver only) or see ๐Ÿ”ง macOS uvx Troubleshooting

Advanced Docker Setup (for remote MCP clients)

# Run SSE server on port 8000 (uses environment variable)
docker run -d -p 8000:8000 -e TRANSPORT_MODE=sse \
  ghcr.io/dmitryanchikov/mcp-optimizer:latest

# Or with CLI argument and custom port
docker run -d -p 9000:9000 ghcr.io/dmitryanchikov/mcp-optimizer:latest \
  python -m mcp_optimizer.main --transport sse --host 0.0.0.0 --port 9000

# Check server status
docker logs <container-name>

# Verify SSE endpoint (should show event stream)
curl -i http://localhost:8000/sse

SSE Endpoint: http://localhost:8000/sse (Server-Sent Events for MCP communication)

Cursor Integration
  1. Install the MCP extension in Cursor
  2. Add mcp-optimizer to your workspace settings (Docker recommended):
{
  "mcp.servers": {
    "mcp-optimizer": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "ghcr.io/dmitryanchikov/mcp-optimizer:latest",
        "python", "main.py"
      ]
    }
  }
}

Alternative configurations:

// Using pip installation
{
  "mcp.servers": {
    "mcp-optimizer": {
      "command": "mcp-optimizer"
    }
  }
}

// Using uvx (limited functionality on macOS)
{
  "mcp.servers": {
    "mcp-optimizer": {
      "command": "uvx",
      "args": ["mcp-optimizer"]
    }
  }
}
Other LLM Clients

For other MCP-compatible clients (Continue, Cody, etc.), use similar configuration patterns. Recommended priority:

  1. Docker (maximum stability across platforms)
  2. pip + venv (standard Python approach)
  3. uvx (quick testing, limited on macOS)

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
Local Package Build and Run

For testing and development, you can build the package locally and run it with uvx:

# Build the package locally
uv build

# Run with uvx from local wheel file
uvx --from ./dist/mcp_optimizer-0.3.9-py3-none-any.whl mcp-optimizer

# Or run with help to see available options
uvx --from ./dist/mcp_optimizer-0.3.9-py3-none-any.whl mcp-optimizer --help

# Test the local package with a simple MCP message
echo '{"jsonrpc": "2.0", "method": "initialize", "params": {"protocolVersion": "2024-11-05", "capabilities": {}, "clientInfo": {"name": "test", "version": "1.0"}}, "id": 1}' | uvx --from ./dist/mcp_optimizer-0.3.9-py3-none-any.whl mcp-optimizer

Note: The local build creates both wheel (.whl) and source distribution (.tar.gz) files in the dist/ directory. The wheel file is recommended for uvx installation as it's faster and doesn't require compilation.

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
Transport Modes

MCP Optimizer supports two MCP transport protocols:

  • STDIO: Standard input/output for direct MCP client integration (Claude Desktop, Cursor, etc.)
  • SSE: Server-Sent Events over HTTP for web-based MCP clients and remote integrations

STDIO Transport (Default - for MCP clients like Claude Desktop)

# Default STDIO mode for MCP protocol
uvx mcp-optimizer
# or
uvx mcp-optimizer --transport stdio
# or
uv run python -m mcp_optimizer.main --transport stdio
# or
python main.py

SSE Transport (for remote MCP clients)

# SSE mode for remote MCP clients (default port 8000)
uvx mcp-optimizer --transport sse
# or
uv run python -m mcp_optimizer.main --transport sse

# Custom host and port
uvx mcp-optimizer --transport sse --host 0.0.0.0 --port 9000
# or
uv run python -m mcp_optimizer.main --transport sse --host 0.0.0.0 --port 9000

# With debug mode
uvx mcp-optimizer --transport sse --debug --log-level DEBUG

Available CLI Options

# Show all available options
uvx mcp-optimizer --help

# Options:
#   --transport {stdio,sse}    MCP transport protocol (default: stdio)
#   --port PORT               Port for SSE transport (default: 8000)
#   --host HOST               Host for SSE transport (default: 127.0.0.1)
#   --debug                   Enable debug mode
#   --reload                  Enable auto-reload for development
#   --log-level {DEBUG,INFO,WARNING,ERROR}  Logging level (default: INFO)
#
# Environment Variables:
#   TRANSPORT_MODE={stdio,sse}  Override transport mode
#   SERVER_HOST=0.0.0.0        Override server host
#   SERVER_PORT=8000           Override server port

๐Ÿ”ง Platform Compatibility & Troubleshooting

macOS Compatibility

โœ… Full Functionality:

  • Homebrew + pip: brew install or-tools && pip install mcp-optimizer
  • Virtual environments: python -m venv venv && source venv/bin/activate && pip install ortools mcp-optimizer
  • Docker: Full OR-Tools support in containers

โš ๏ธ Limited Functionality:

  • uvx (isolated environments): Only PuLP solver available due to OR-Tools native library paths
  • Fallback behavior: Automatically switches to PuLP when OR-Tools unavailable

Common Issues & Solutions:

  1. OR-Tools "Library not loaded" error:

    # Solution: Install via Homebrew
    brew install or-tools
    # Then use regular pip/venv instead of uvx
    
  2. uvx shows OR-Tools warnings:

    WARNING: OR-Tools not available: No module named 'ortools'
    

    This is expected - uvx provides fallback functionality with PuLP solver.

  3. Best practices for macOS:

    • Use Docker for production deployments
    • Use Homebrew + pip for development
    • Use uvx for quick testing (limited functionality)

Linux/Windows Compatibility

โœ… Full Functionality:

  • uvx: Works out of the box with OR-Tools
  • pip: Standard installation
  • Docker: Recommended for production

Solver Availability by Platform

PlatformuvxpipDocker
macOSPuLP onlyโœ… Fullโœ… Full
Linuxโœ… Fullโœ… Fullโœ… Full
Windowsโœ… Fullโœ… Fullโœ… Full

Solver Features:

  • OR-Tools: Advanced algorithms (CP-SAT, routing, scheduling)
  • PuLP: Basic linear programming, reliable fallback

๐Ÿ”ง macOS uvx Troubleshooting

Problem: OR-Tools Library Issues with uvx

Common Error Messages:

Library not loaded: /Users/corentinl/work/stable/temp_python3.13/lib/libscip.9.2.dylib
ImportError: No module named 'ortools'
WARNING: OR-Tools not available

Root Cause: OR-Tools binary wheels contain hardcoded library paths that fail in uvx isolated environments. This is a macOS-specific issue due to how uvx isolates dependencies.

๐Ÿ“Š Functionality Impact by Installation Method

โœ… Available with uvx + fallback (PuLP solver only):

  • Linear Programming - Basic optimization, simplex method
  • Financial Optimization - Portfolio optimization, risk management
  • Production Planning - Resource allocation, inventory management

โŒ Lost with uvx (requires OR-Tools):

  • Assignment Problems - Hungarian algorithm, transportation problems
  • Integer Programming - Mixed-integer, binary programming (SCIP/CBC)
  • Knapsack Problems - Discrete optimization, multiple variants
  • Vehicle Routing - TSP, CVRP, time windows (constraint programming)
  • Job Scheduling - CP-SAT solver, resource planning

๐Ÿ› ๏ธ Solutions (in order of preference)

1. Automated Fix Script (Recommended)
# Smart adaptive script - no hardcoded versions!
# Automatically detects your system libraries and Python versions
./scripts/fix_macos_uvx.sh

# Then uvx works with full functionality
uvx mcp-optimizer --transport stdio
2. Manual Fix
# Install system dependencies
brew install or-tools scip

# Create symlink for hardcoded path
sudo mkdir -p /Users/corentinl/work/stable/temp_python3.13/lib/
sudo ln -sf /opt/homebrew/lib/libscip.9.2.dylib /Users/corentinl/work/stable/temp_python3.13/lib/libscip.9.2.dylib

# Test fix
uvx mcp-optimizer --help
3. Use pip (Always Works)
# Install dependencies first
brew install or-tools

# Install package
pip install mcp-optimizer
mcp-optimizer
4. Use Docker (Production Ready)
docker run -p 8000:8000 mcp-optimizer

๐ŸŽฏ 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

Automated Test Scripts

Quick Testing:

# Test local package build and functionality
./scripts/test_local_package.sh

# Test Docker container build and functionality  
./scripts/test_docker_container.sh

# Run comprehensive test suite (both package and Docker)
./scripts/test_all.sh

# Run only specific tests
./scripts/test_all.sh --skip-docker    # Skip Docker tests
./scripts/test_all.sh --skip-package   # Skip package tests

Manual Testing:

# Run simple functionality tests
uv run python tests/test_integration/comprehensive_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

Test Scripts Features:

  • โœ… Local Package Testing: Build, STDIO/SSE modes, CLI functionality
  • โœ… Docker Container Testing: Image build, environment variables, health checks
  • โœ… Comprehensive Suite: Parallel execution with detailed reporting
  • โœ… Automatic Cleanup: Processes and containers cleaned up after tests
  • โœ… Cross-Platform: Works on macOS, Linux (requires Docker for container tests)

Requirements:

  • For local tests: uv, curl, lsof, gtimeout/timeout
  • For Docker tests: docker + local requirements
  • macOS: brew install coreutils (for gtimeout)

CI/CD Integration:

# GitHub Actions example
- name: Test Package
  run: ./scripts/test_local_package.sh
- name: Test Docker
  run: ./scripts/test_docker_container.sh

๐Ÿ“Š 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

๐Ÿ—๏ธ Architecture

mcp-optimizer/
โ”œโ”€โ”€ LICENSE                     # MIT License
โ”œโ”€โ”€ README.md                   # Project documentation
โ”œโ”€โ”€ CHANGELOG.md               # Release notes
โ”œโ”€โ”€ CONTRIBUTING.md            # Contribution guidelines
โ”œโ”€โ”€ pyproject.toml             # Python project configuration
โ”œโ”€โ”€ uv.lock                    # Dependency lock file
โ”œโ”€โ”€ main.py                    # Entry point
โ”œโ”€โ”€ Dockerfile                 # Main Docker configuration
โ”œโ”€โ”€ docker-compose.yml         # Multi-service setup
โ”œโ”€โ”€ .dockerignore             # Docker ignore rules
โ”œโ”€โ”€ .gitignore                # Git ignore rules
โ”œโ”€โ”€ .python-version           # Python version specification
โ”œโ”€โ”€ src/mcp_optimizer/        # Main source code
โ”‚   โ”œโ”€โ”€ __init__.py
โ”‚   โ”œโ”€โ”€ __main__.py           # Module entry point
โ”‚   โ”œโ”€โ”€ main.py               # Application entry point
โ”‚   โ”œโ”€โ”€ mcp_server.py         # MCP server implementation
โ”‚   โ”œโ”€โ”€ config.py             # Configuration management
โ”‚   โ”œโ”€โ”€ tools/                # 9 categories of optimization tools
โ”‚   โ”‚   โ”œโ”€โ”€ linear_programming.py
โ”‚   โ”‚   โ”œโ”€โ”€ assignment.py
โ”‚   โ”‚   โ”œโ”€โ”€ knapsack.py
โ”‚   โ”‚   โ”œโ”€โ”€ routing.py
โ”‚   โ”‚   โ”œโ”€โ”€ scheduling.py
โ”‚   โ”‚   โ”œโ”€โ”€ financial.py
โ”‚   โ”‚   โ””โ”€โ”€ production.py
โ”‚   โ”œโ”€โ”€ solvers/              # PuLP and OR-Tools integration
โ”‚   โ”‚   โ”œโ”€โ”€ pulp_solver.py
โ”‚   โ”‚   โ””โ”€โ”€ ortools_solver.py
โ”‚   โ”œโ”€โ”€ schemas/              # Pydantic validation schemas
โ”‚   โ””โ”€โ”€ utils/                # Utility functions
โ”œโ”€โ”€ tests/                    # Comprehensive test suite
โ”‚   โ”œโ”€โ”€ test_tools/           # Tool-specific tests
โ”‚   โ”œโ”€โ”€ test_solvers/         # Solver tests
โ”‚   โ””โ”€โ”€ test_integration/     # Integration tests
โ”œโ”€โ”€ scripts/                  # Automation scripts
โ”œโ”€โ”€ examples/                 # Usage examples and prompts
โ”‚   โ”œโ”€โ”€ en/                   # English examples
โ”‚   โ””โ”€โ”€ ru/                   # Russian examples
โ”œโ”€โ”€ k8s/                      # Kubernetes deployment manifests
โ””โ”€โ”€ monitoring/               # Grafana/Prometheus setup
    โ””โ”€โ”€ grafana/
        โ””โ”€โ”€ datasources/

๐Ÿงช Test Results

โœ… Comprehensive Test Suite

๐Ÿงช Starting Comprehensive MCP Optimizer Tests
==================================================
โœ… Server Health PASSED
โœ… Linear Programming PASSED
โœ… Assignment Problems PASSED  
โœ… Knapsack Problems PASSED
โœ… Routing Problems PASSED
โœ… Scheduling Problems PASSED
โœ… Financial Optimization PASSED
โœ… Production Planning PASSED
โœ… Performance Test PASSED

๐Ÿ“Š Test Results: 9 passed, 0 failed
๐ŸŽ‰ All tests passed! MCP Optimizer is ready for production!

โœ… Unit Tests

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

๐Ÿ“ˆ 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: ๐Ÿš€ Excellent!

๐Ÿ”ง 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

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

๐Ÿ“‹ Requirements

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

๐Ÿš€ 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

๐ŸŽฏ Project Status

โœ… PRODUCTION READY ๐Ÿš€

  • 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

๐Ÿ“– Usage Examples

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

Available Examples

  • ๐Ÿ“Š Linear Programming ( | )
    • Production optimization, diet planning, transportation, blending problems
  • ๐Ÿ‘ฅ Assignment Problems ( | )
    • Employee-project assignment, machine-order allocation, task distribution
  • ๐Ÿ’ฐ Portfolio Optimization ( | )
    • 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

๐Ÿ”„ 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

๐Ÿš€ 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 โ†’ main
gh pr create --base main --head release/v0.3.0 --title "Release v0.3.0"
3. Merge PR - DONE! ๐ŸŽ‰

After PR merge, automatically happens:

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

NO NEED to run manual_finalize_release.py manually anymore!

๐Ÿ”’ Secure Detection: Uses hybrid approach combining GitHub branch protection with automated release detection. See for details.

Automated Release Pipeline

The CI/CD pipeline automatically handles:

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

CI/CD Pipeline

The GitHub Actions workflow automatically:

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

Requirements for PyPI Publication

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

๐Ÿ› ๏ธ 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('โœ… MCP Optimizer works!')"

๐Ÿค Contributing

We welcome contributions! Please see for guidelines.

Git Flow Policy

This project follows a standard Git Flow workflow:

  • Feature branches โ†’ develop branch
  • Release branches โ†’ main branch
  • Hotfix branches โ†’ main and develop branches

๐Ÿ“š Documentation:

  • - Complete development workflow and Git Flow policy
  • - How releases are created and automated
  • - 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!)

๐Ÿ“„ License

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

๐Ÿ™ Acknowledgments

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

๐Ÿ“ž Support


Made with โค๏ธ for the optimization community

๐Ÿ“Š Docker Image Size Analysis

The MCP Optimizer Docker image has been optimized to balance functionality and size:

ComponentSize% of TotalDescription
Python packages (/venv)237.0 MB42.8%Virtual environment with dependencies
System libraries (/usr)173.2 MB31.3%Base Debian system + Python
Other137.4 MB24.8%Base image, filesystem
Configuration (/var, /etc)6.2 MB1.1%System settings
Application code (/code)0.2 MB0.04%MCP Optimizer source code

Key Dependencies by Size

  • OR-Tools: 75.0 MB (27.8% of venv) - Critical optimization solver (requires pandas + numpy)
  • pandas: 45.0 MB (16.7% of venv) - Required by OR-Tools for data operations
  • NumPy: 24.0 MB (8.9% of venv) - Required by OR-Tools for numerical computing
  • PuLP: 34.9 MB (12.9% of venv) - Linear programming solver
  • FastMCP: 15.2 MB (5.6% of venv) - MCP server framework
  • Pydantic: 12.8 MB (4.7% of venv) - Data validation

Dependencies Analysis

  • Core packages cannot be reduced further: OR-Tools (our main optimization engine) requires both pandas and numpy as mandatory dependencies
  • Optional examples moved: Additional packages for examples (streamlit, plotly) moved to [examples] extra
  • Minimal core impact: Moving examples to optional dependencies only affects development/demo usage

Image Optimization

  • Current optimized size: ~420MB
  • Core functionality: Includes all necessary dependencies for production optimization
  • Example support: Install with [examples] extra for additional demo functionality
  • OR-Tools constraint: Cannot remove pandas/numpy due to hard dependency requirements