Bright-L01/networkx-mcp-server

NetworkX MCP Server

A comprehensive Model Context Protocol (MCP) server providing advanced graph analysis capabilities using NetworkX.

🚀 Features

• Complete MCP Implementation: Full Model Context Protocol support with Tools, Resources, and Prompts
• Modular Architecture: Clean, maintainable codebase with 35+ focused modules
• Advanced Graph Analysis: Comprehensive suite of graph algorithms and analytics
• Production Ready: Enterprise-grade security, monitoring, and scalability features
• Developer Friendly: Extensive documentation, testing, and development tools

🏗️ Architecture

The server follows a clean modular architecture:

├── Core Layer          # Basic graph operations and MCP server
├── Handler Layer       # Function organization and re-exports  
├── Advanced Layer      # Specialized algorithms and features
└── Supporting Layer    # Monitoring, security, and infrastructure

See for detailed architectural documentation.

📦 Quick Start

Installation

git clone https://github.com/username/networkx-mcp-server.git
cd networkx-mcp-server
pip install -e .

Basic Usage

from networkx_mcp.server import create_graph, add_nodes, add_edges

# Create a graph
result = create_graph("my_graph", "undirected")

# Add nodes and edges
add_nodes("my_graph", ["A", "B", "C"])
add_edges("my_graph", [("A", "B"), ("B", "C")])

Running the Server

# Start the MCP server
python -m networkx_mcp

# Or use the development script
./run_tests.sh

🧪 Testing

The project maintains 80%+ test coverage with comprehensive test suites:

# Run all tests
pytest

# Run with coverage
pytest --cov=src/networkx_mcp --cov-report=html

# Run specific test categories
pytest tests/unit/          # Unit tests
pytest tests/integration/   # Integration tests
pytest tests/performance/   # Performance tests

📖 Documentation

• - Complete system architecture
• - Detailed module organization
• - Developer handbook
• - Detailed API reference

🤝 Contributing

We welcome contributions! Please see our for:

• Setting up the development environment
• Code standards and conventions
• Testing requirements
• Submission guidelines

Quick Development Setup

# Install development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

# Run the test suite
pytest

🏆 Quality Standards

This project maintains high quality standards:

• Code Quality: Automated formatting with ruff, black, and isort
• Type Safety: Comprehensive type hints with mypy validation
• Security: Bandit security scanning and vulnerability checks
• Testing: 80%+ test coverage with multiple test categories
• Documentation: Comprehensive documentation and examples

📋 Requirements

• Python 3.11+
• NetworkX 3.0+
• FastMCP (or compatible MCP implementation)

See for complete dependency list.

🚀 Deployment

Docker

# Build and run with Docker
docker build -t networkx-mcp-server .
docker run -p 8000:8000 networkx-mcp-server

Kubernetes

# Deploy to Kubernetes
kubectl apply -f k8s/

See for production deployment guides.

📊 Performance

The server is optimized for performance:

• Modular Design: Efficient memory usage and fast load times
• Algorithm Optimization: Optimized implementations for large graphs
• Monitoring: Built-in performance metrics and health checks
• Scalability: Stateless design supporting horizontal scaling

🔒 Security

Security is a top priority:

• Input Validation: Comprehensive input sanitization and validation
• Access Control: Authentication and authorization layers
• Audit Logging: Complete audit trail for security events
• Vulnerability Scanning: Automated dependency vulnerability checks

📈 Monitoring

Built-in observability features:

• Health Checks: Comprehensive health monitoring endpoints
• Metrics: Performance and usage metrics collection
• Tracing: Distributed tracing support
• Logging: Structured logging with configurable levels

🗂️ Project Structure

networkx-mcp-server/
├── src/networkx_mcp/       # Main source code
│   ├── core/               # Core graph operations
│   ├── handlers/           # Function handlers
│   ├── advanced/           # Advanced algorithms
│   ├── monitoring/         # Monitoring and observability
│   └── security/           # Security features
├── tests/                  # Comprehensive test suite
├── docs/                   # Documentation
├── scripts/                # Development and deployment scripts
└── examples/               # Usage examples

📜 License

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

🙏 Acknowledgments

• NetworkX team for the excellent graph analysis library
• FastMCP team for the Model Context Protocol implementation
• Contributors and users of this project

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation:

Built with ❤️ for the graph analysis community