Refining AI Access to FHIR Data: A Technical Deep Dive into Building an MCP Server
Part 1: Why We Built an MCP Server — And
Summary
To solve the problem of AI 'hallucination' when querying vast, versioned FHIR specifications in healthcare IT, a development team built a Model Context Protocol (MCP) server. The article analyzes the shortcomings of traditional RAG and API designs, detailing why MCP was adopted to provide structured data access and outlining the local-first architecture.
Details
In healthcare tech, AI assistants frequently hallucinate when asked questions about massive, versioned specifications like FHIR—such as inventing non-existent fields or confusing profiles. To address this, the authors developed a system allowing the AI to reference actual, versioned FHIR packages stored locally, rather than relying on web searches or training data. They chose Model Context Protocol (MCP) because it is specifically designed to give AI models structured access to external data and tools. Unlike traditional RAG, which loses structural relationships when embedding highly nested JSON, or simple REST APIs, which break the conversational flow, MCP provides a clean interface of callable 'Tools' (functions), readable 'Resources' (via URI), and reusable 'Prompts.' The architecture adheres to a strict 'local-first, read-only' principle, eliminating network calls. For performance, they adopted an 'index first, serve second' approach: metadata from the entire FHIR package is extracted and stored in SQLite. This allows the runtime server to perform fast, predictable SQL queries without slow filesystem scanning.
Technology Note
FHIR（Fast Healthcare Interoperability Resources）は医療データ交換の国際標準。このエントリの関連技術: FHIR
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