Healthcare MCP Server

The Healthcare MCP Server is a purpose‑built Model Context Protocol (MCP) service that bridges AI assistants with authoritative medical data sources. By exposing a rich set of tools—ranging from FDA drug lookups to PubMed literature searches and clinical trial queries—the server empowers developers to embed real‑time, evidence‑based health information into conversational agents. Instead of relying on static knowledge bases or third‑party APIs that may lag behind current medical guidelines, this MCP server pulls directly from up‑to‑date databases such as the FDA’s drug registry, PubMed, Health.gov, and NCBI Bookshelf. The result is a single, cohesive interface that delivers accurate medical content without the overhead of managing multiple external integrations.

For developers building AI‑driven health applications, this server offers a streamlined workflow. The MCP protocol automatically handles context passing and tool invocation, allowing an assistant to request a drug fact or calculate BMI with a single command. Built‑in caching and connection pooling reduce latency and API costs, while the optional usage tracking provides insights into how often each tool is called—useful for monitoring compliance and performance. The server’s dual interface support (stdio for CLI agents and HTTP/SSE for web clients) means it can be deployed in a variety of environments, from local development to cloud‑based services.

Key capabilities include:

• FDA Drug Information – precise, parsed drug data directly from the FDA database.
• PubMed & medRxiv Search – comprehensive literature retrieval for peer‑reviewed and preprint research.
• Health.gov Topics – up‑to‑date, evidence‑based health information via the latest API.
• Clinical Trials Explorer – search for ongoing and completed trials with current parameters.
• Medical Terminology Lookup – ICD‑10 code definitions and terminology references.
• DICOM Metadata Extraction – pull imaging metadata for diagnostic workflows.
• Medical Calculator – quick BMI calculations for patient assessments.

These tools enable a wide range of real‑world scenarios: a virtual triage assistant can instantly fetch drug interactions; a research chatbot can surface the latest studies on a disease; an electronic health record (EHR) integration can annotate patient notes with standardized terminology—all without manual API management. By encapsulating these functions behind a single MCP server, developers can focus on crafting user experiences rather than handling disparate data sources.

In practice, a developer would install the server (via DXT or npm), launch it in stdio mode for local testing, and then point an AI assistant—such as Claude Desktop—to the MCP endpoint. Once connected, the assistant can call any of the exposed tools using natural language prompts, and the server returns structured responses that can be directly rendered or further processed. The combination of authoritative data, efficient caching, and seamless MCP integration makes the Healthcare MCP Server a standout solution for building trustworthy, data‑driven health AI applications.