Networkx Mcp Server

The first NetworkX integration for Model Context Protocol, enabling graph analysis and visualization directly in AI conversations. Supports 13 operations including centrality algorithms, community detection, PageRank, and graph visualization.

Updated 1 week ago
Added Jun 27, 2025

Docs & Usage Guide

NetworkX MCP Server

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

๐Ÿš€ 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

๐Ÿ—๏ธ Architecture

The server follows a clean modular architecture:

โ”œโ”€โ”€ Core Layer          # Basic graph operations and MCP server
โ”œโ”€โ”€ Handler Layer       # Function organization and re-exports
โ”œโ”€โ”€ Advanced Layer      # Specialized algorithms and features
โ””โ”€โ”€ Supporting Layer    # Monitoring, security, and infrastructure

See ARCHITECTURE.md for detailed architectural documentation.

๐Ÿ“ฆ 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

๐Ÿงช 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

๐Ÿ“– Documentation

๐Ÿค Contributing

We welcome contributions! Please see our Development Guide 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

๐Ÿ† 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

๐Ÿ“‹ Requirements

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

See pyproject.toml for complete dependency list.

๐Ÿš€ 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 deployment documentation for production deployment guides.

๐Ÿ“Š 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

๐Ÿ”’ 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

๐Ÿ“ˆ 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

๐Ÿ—‚๏ธ Project Structure

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

๐Ÿ“œ License

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

๐Ÿ™ Acknowledgments

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

๐Ÿ“ž Support


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