FHIR MCP Server
Provides safe read-only access to synthetic healthcare data via FHIR R4, enabling AI assistants to search patients, retrieve vitals, and summarize conditions using a public sandbox.
README
FHIR MCP Server
An MCP (Model Context Protocol) server that gives AI assistants safe, read-only access to healthcare data using the FHIR R4 standard — built on a public sandbox, so it's completely safe to run, share, and extend.
Instead of an AI assistant guessing at what a "patient," "observation," or "condition" looks like, this server lets it query real FHIR-shaped data through a small set of well-defined, guardrailed tools.
⚠️ Data note: This project connects to the SMART Health IT public sandbox (
r4.smarthealthit.org), which serves synthetic, non-real test data for development purposes. No real patient data is used or stored anywhere in this project. This is a technical demonstration of AI-assisted healthcare data workflows — not a clinical or diagnostic tool.
Why this project exists
Healthcare data is one of the most valuable — and most sensitive — domains for AI to work in. Most public MCP examples connect AI to generic APIs (weather, GitHub, Slack). This one demonstrates something closer to real enterprise work: giving an AI assistant controlled, read-only access to structured clinical data, with the same instincts you'd want in a production system — least privilege, hard limits, and no write access at all.
It pairs naturally with an orchestrating agent: point an AI assistant (Claude, or any MCP-compatible client) at this server, and it can look up patients, pull their vitals, and summarize conditions in plain language — all through auditable, typed tool calls instead of free-form scraping.
Tools
| Tool | What it does |
|---|---|
search_patients |
Search the sandbox for patients by name |
get_patient |
Get demographic details for a patient by id |
get_observations |
Get recent vitals/lab observations for a patient |
summarize_conditions |
Get a plain-language summary of a patient's recorded conditions |
All tools are read-only — there is no create, update, or delete capability anywhere in this server, by design.
Guardrails
This server is built with the same discipline you'd want in any production integration:
- Read-only, always. No write, update, or delete operations exist in the codebase.
- Result caps. Every query is capped (default 10, max 20 results) to prevent runaway responses.
- Request timeouts. All outbound FHIR requests time out after 10 seconds.
- Sandbox-only data source. Points exclusively at a public, synthetic FHIR test server — never a production or real clinical system.
Getting started
Prerequisites
- Node.js 18+
- An MCP-compatible client (e.g. Claude Desktop, or any client supporting the MCP stdio transport)
Install & build
git clone https://github.com/brianbastian01/fhir-mcp-server.git
cd fhir-mcp-server
npm install
npm run build
Run it
npm start
The server communicates over stdio, so in practice you'll point your MCP client at it rather than running it standalone. For example, in Claude Desktop's config:
{
"mcpServers": {
"fhir": {
"command": "node",
"args": ["/absolute/path/to/fhir-mcp-server/dist/index.js"]
}
}
}
Restart your MCP client, and the four tools above will be available for the AI to call.
Try it
Once connected, ask your AI assistant something like:
"Search the FHIR sandbox for patients named Smith, then summarize the conditions for the first result."
The assistant will call search_patients, then summarize_conditions, and give you a plain-language answer — all backed by real tool calls you can inspect.
Project structure
fhir-mcp-server/
├── src/
│ └── index.ts # Server setup + all four tool definitions
├── dist/ # Compiled output (generated by `npm run build`)
├── package.json
├── tsconfig.json
├── LICENSE
└── README.md
Roadmap / ideas for extending this
- Add a
medicationstool (FHIRMedicationRequestresource) - Add pagination support for large result sets
- Add an in-memory cache to reduce repeated calls to the sandbox
- Swap the sandbox URL for a real FHIR server behind proper auth (OAuth2/SMART on FHIR) for a production-grade version
- Pair with a small orchestrating agent that chains these tools automatically
About
Built by Brian Bastian, Solution Architect with 14+ years in enterprise software, cloud architecture, and — more recently — AI-assisted engineering, agents, and MCP servers.
License
MIT — see LICENSE.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.