MCP FHIR Server – Overview

The MCP FHIR Server bridges the gap between conversational AI assistants and clinical data stored in a FHIR (Fast Healthcare Interoperability Resources) server. By exposing FHIR operations as MCP tools, the server enables an AI assistant to query and manipulate patient records, appointments, or other healthcare resources without writing custom API code. This eliminates the need for developers to implement bespoke integrations for each FHIR endpoint, accelerating prototyping and reducing boilerplate.

At its core, the server implements two essential tools: Create FHIR Resource and Read FHIR Resource. The Create tool accepts a (e.g., Patient, Observation) and the JSON representation of that resource (). It forwards the payload to the underlying FHIR server, handling serialization and HTTP communication. The Read tool takes a and an identifier () to fetch the desired resource. By exposing these actions through MCP, an AI assistant can invoke them directly in a conversation—“Create a new Patient with the following details” or “Show me appointment 12345”—and receive structured responses that can be parsed or displayed.

Key features include:

• Standardized tool definitions: Each operation is defined with clear input parameters and expected output, making it trivial for MCP clients to discover and use.
• Token‑based authentication: The server expects an OAuth token supplied by the client, allowing secure access to protected FHIR endpoints without handling complex credential flows internally.
• Lightweight Express implementation: Built on a minimal Node.js stack, the server can be deployed quickly in development or testing environments.
• Extensibility: While currently limited to create and read, the architecture can be expanded to support update, delete, or search operations as needed.

Real‑world scenarios that benefit from this server include:

• Clinical decision support: A nurse’s assistant chatbot can fetch patient history or schedule appointments on demand.
• Health data ingestion: An IoT device management system can create Observation resources directly from sensor streams via an AI orchestrator.
• Patient portals: Voice‑enabled interfaces can read appointment details or confirm bookings without custom code.

Integration with AI workflows is straightforward: an MCP‑compliant assistant discovers the server’s capabilities, sends a tool invocation with the required parameters, and receives the FHIR response. The assistant can then format the data for the user or trigger additional downstream processes. This seamless interaction turns raw FHIR APIs into conversational actions, dramatically lowering the barrier for developers to embed sophisticated healthcare data handling in AI applications.