JamesANZ/medical-mcp
JamesANZ/medical-mcp is a Model Context Protocol (MCP) server that provides AI assistants with direct access to authoritative medical information. Running entirely locally, it bridges the gap between AI coding environments like Cursor and Claude Desktop, and trusted medical data sources including FDA, WHO, PubMed, RxNorm, Google Scholar, AAP, and pediatric journals. The server requires no API keys, no cloud storage, and no external logging, ensuring complete privacy and data sovereignty. With this MCP server, developers can integrate comprehensive drug information, health statistics, medical literature, clinical guidelines, and pediatric resources into their AI workflows. It supports queries for drug interactions, FDA labels, PubMed articles, and more, all through a simple local interface. The installation is streamlined: one-click setup in Cursor or a quick manual configuration, lowering the barrier for non-experts to leverage medical data. The server is built with TypeScript and distributed via npm, making it lightweight and cross-platform. It leverages the MCP standard to expose tools that AI models can call, enabling dynamic retrieval of up-to-date medical knowledge during conversations. By keeping all data processing local, it addresses compliance concerns common in healthcare applications. JamesANZ/medical-mcp is open source and community-driven, with active maintenance and contributions. It is part of a growing ecosystem of MCP servers that extend AI capabilities into specialized domains, and it is designed to be extensible for additional medical APIs and custom needs.
- Category
- Developer Tools
- Pricing
- Open Source
Problem solved
AI assistants often lack access to specialized, authoritative medical data, forcing developers to manually integrate APIs, manage API keys, and worry about data privacy. This MCP server solves that by providing a drop-in, local-only bridge between AI models and curated medical sources. It enables healthcare developers, researchers, and clinicians to safely query drug information, medical literature, and guidelines without external dependencies or privacy risks.
Use cases
- A developer builds a clinical decision support bot that checks drug interactions using FDA and RxNorm data during patient conversations.
- A medical researcher asks an AI assistant to find recent PubMed publications on a specific disease and get summaries without leaving the IDE.
- A healthcare educator uses the server to provide students with instant access to pediatric guidelines and AAP resources.
- A health-tech startup integrates the MCP server to prototype a symptom checker that references WHO statistics and medical literature.
- A data analyst extracts global health indicators from WHO and cross-references with drug usage data for an internal report.
Audience
- Developers
- Researchers
- Data Scientists
- Enterprise
Pros
- Fully local execution guarantees data privacy and avoids external API costs.
- Zero configuration required — no API keys or sign-ups.
- Aggregates multiple authoritative medical sources in one unified interface.
- Simple installation and compatibility with popular AI tools like Cursor and Claude Desktop.
- Open source and extensible for custom medical data sources.
Cons
- Limited to MCP-compatible clients; not usable as a standalone API server.
- Depends on the availability and completeness of the external medical data sources it queries.
- Requires local runtime and basic familiarity with npm or developer tools.
Official website: https://github.com/JamesANZ/medical-mcp
Canonical LambdaBase page: https://www.lambdabase.com/tools/jamesanz-medical-mcp