NetworkX MCP Server is a complete Model Context Protocol (MCP) server implementation designed to provide advanced NetworkX-based graph analysis capabilities for AI conversations. The project is positioned as "the first NetworkX MCP server for graph analysis in AI conversations," offering a community edition and an enterprise edition.
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Main Capabilities The server provides comprehensive algorithm and tool support around graph analysis, including centrality algorithms, community detection, PageRank, and graph visualization, and supports the overall integration of graph algorithms and network analysis. It fully implements the MCP protocol, supporting three types of capabilities: Tools, Resources, and Prompts.
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Architecture and Quality The server adopts a clear modular architecture, and the codebase consists of more than 35 focused modules, making it easy to maintain. The project maintains over 80% test coverage and is equipped with a comprehensive test suite. Quality assurance includes: automated formatting using ruff, black, and isort; type checking via mypy; security scanning and vulnerability checks using Bandit; and detailed documentation and examples. The project uses the MIT License.
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Production and Security Features The project is positioned as production-ready, with enterprise-grade security, monitoring, and scalability. Security aspects include: comprehensive input sanitization and validation, authentication and authorization layers, complete audit logs for security events, and automated dependency vulnerability scanning. Observability provides: health check endpoints, performance and usage metrics collection, distributed tracing support, and structured logging with configurable levels. In terms of performance, algorithms are optimized for large graphs, a stateless design is adopted to support horizontal scaling, and built-in performance metrics and health checks are provided.
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Usage and Dependencies The server depends on FastMCP (or a compatible MCP implementation); the complete dependency list is in pyproject.toml; production deployment guidance is in the project's deployment documentation. The project provides materials such as an architecture overview, module structure, development guide, and API documentation, and welcomes contributors; the contribution process includes development environment setup, code standards, testing requirements, and submission guidelines. Related resources include GitHub Issues, GitHub Discussions, and project documentation.
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Applicable Scenarios It is suitable for scenarios that require graph structure analysis in AI conversations, such as network analysis, graph algorithm computation, community discovery, node importance ranking (PageRank, centrality), andgraph visualization, etc.
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Limitations Project materials do not provide specific installation commands, example deployment steps, specific functional differences between the enterprise edition and the community edition, or pricing or charging information; these contents are not currently provided.
