Langcare MCP Fhir

🚀 LangCare MCP FHIR Server

Enterprise-grade MCP Server for FHIR-based EMRs, designed for robust deployments in agentic AI platforms.

This is an enterprise-grade MCP Server tailored for FHIR-based EMRs, specifically designed for reliable deployments in agentic AI platforms. It is fully written in Go, offering enterprise-grade security and generic FHIR operations that are compatible with any FHIR R4 resource type.

It comes with a 40+ Clinical Skills Library — a collection of agent-agnostic workflow guides covering various aspects such as medication management, lab interpretation, clinical decision support, documentation, and population health.

⭐ New: LangCare now supports MCP Apps — interactive clinical UIs that are directly embedded in the server. Learn more.

⭐ New: Healthcare Voice Agent — a real-time voice AI that enables patients to inquire about their health records and receive spoken responses from their EMR, powered by PipeCat + LangCare MCP.

⭐ New: LangCare CLI — a Python CLI that wraps FHIR tools (fhir_search, fhir_read, fhir_create, fhir_update) over HTTP for AI agent frameworks that do not natively support MCP (e.g., LangChain, smolagents, CrewAI, AutoGen). It handles the MCP session handshake internally and outputs clean JSON to stdout.

langcare.ai

🚀 Quick Start

Installation

Install via npm:

npm install -g @langcare/langcare-mcp-fhir

Or use directly without installation:

npx @langcare/langcare-mcp-fhir -config /path/to/config.yaml

Quick Configuration

LangCare MCP FHIR connects Claude to your FHIR-based EMR system. You need a YAML configuration file pointing to your backend.

1. Get a Config Template

Choose your backend:

• EPIC: config.epic.example.yaml
• Cerner: config.cerner.example.yaml
• GCP Healthcare API: config.gcp.example.yaml
• Any FHIR R4 Server: config.base.example.yaml

2. Configure Claude Desktop

Add to your Claude Desktop config file (~/.config/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "langcare-mcp-fhir": {
      "command": "langcare-mcp-fhir",
      "args": ["-config", "/path/to/your/config.yaml"]
    }
  }
}

On macOS, the config is typically at:

~/Library/Application\ Support/Claude/claude_desktop_config.json

3. Restart Claude Desktop

Close and reopen Claude Desktop. The FHIR tools will now be available.

Need detailed setup help? See the Local Testing Guide.

✨ Features

• Enterprise-grade: Built for robust deployments in agentic AI platforms.
• Generic FHIR operations: Works with any FHIR R4 resource type.
• Clinical Skills Library: 40+ agent-agnostic workflow guides.
• MCP Apps: Interactive clinical UIs embedded in the server.
• Healthcare Voice Agent: Real-time voice AI for patient inquiries.
• LangCare CLI: Python CLI for AI agent frameworks.

📦 Installation

Install via npm:

npm install -g @langcare/langcare-mcp-fhir

Or use directly without installation:

npx @langcare/langcare-mcp-fhir -config /path/to/config.yaml

💻 Usage Examples

Basic Usage - fhir_read

Read a FHIR resource by type and ID.

{
  "resourceType": "Patient",
  "id": "example-123"
}

Basic Usage - fhir_search

Search FHIR resources with query parameters.

{
  "resourceType": "Patient",
  "queryParams": "name=John&birthdate=gt1990-01-01"
}

Basic Usage - fhir_create

Create a new FHIR resource.

{
  "resourceType": "Observation",
  "resource": {
    "resourceType": "Observation",
    "status": "final",
    "code": { ... },
    "subject": { "reference": "Patient/123" }
  }
}

Basic Usage - fhir_update

Update an existing FHIR resource.

{
  "resourceType": "Patient",
  "id": "example-123",
  "resource": {
    "resourceType": "Patient",
    "id": "example-123",
    "name": [{ "family": "Smith" }]
  }
}

📚 Documentation

Getting Started

• 📖 Local Development & Testing Guide - Complete guide for local setup and testing
• 🚀 Installation & Configuration - Quick setup guide above

Agent Integration

• 🤖 Agent Prompt Guide - Complete guide for AI agents using LangCare MCP FHIR (tool examples, workflows, best practices)

Security & Authentication

• 🛡️ Security Documentation - Complete security architecture and HIPAA compliance
• 🔐 EPIC Setup Guide - JWT authentication, key generation, and JWKS registration
• 📋 EPIC Scopes Reference - Complete OAuth2 scopes guide for FHIR resources
• 🔑 Authentication Methods - Supported auth methods

Deployment

• Fly.io Deployment Guide - Remote Streamable HTTP deployment, provider configs, secrets, Docker

Development & Testing

• 🧪 Testing Methods - Claude Desktop, MCP Inspector, manual testing, and automation
• 📦 Project Structure - Directory layout and architecture
• 🔧 Build Commands - Development workflow

🔧 Technical Details

Architecture

This MCP server acts as an intelligent proxy between AI agents and FHIR R4 servers. It exposes 4 generic FHIR operations through the Model Context Protocol (MCP), enabling AI-powered workflows for any FHIR resource type.

Key Design:

• MCP SDK: Official github.com/modelcontextprotocol/go-sdk (Anthropic/Google maintained)
• FHIR Client: Generic HTTP client working with any FHIR R4 server
• Transport: stdio and Streamable HTTP
• Backend: Proxy to existing FHIR server (no database)
• Language: 100% Go for high performance and reliability

Security Architecture

LangCare MCP FHIR implements a two-layer security model for HIPAA-compliant healthcare data access:

┌─────────────┐         ┌──────────────┐         ┌─────────────┐
│   Claude    │ Auth1   │  MCP Server  │ Auth2   │  FHIR API   │
│   Client    │────────▶│   (Go)       │────────▶│   (EMR)     │
└─────────────┘         └──────────────┘         └─────────────┘

Auth1: MCP Client Authentication (Bearer Token/API Key)
Auth2: FHIR Backend Authentication (Bearer/OAuth2/SMART on FHIR)

Security Features

• ✅ TLS 1.3 encryption for HTTP transport
• ✅ PHI Scrubbing in logs (enabled by default)
• ✅ HIPAA-compliant audit logging
• ✅ No persistent PHI storage (stateless proxy)
• ✅ Secrets via environment variables (never in config files)
• ✅ OAuth 2.0 with automatic token refresh
• ✅ mTLS support for service-to-service communication
• ✅ Rate limiting per client

Supported Authentication Methods

• Bearer Token - Simple API key authentication
• OAuth2 - Full OAuth2 flow with token refresh
• SMART on FHIR - EPIC, Cerner, and other EMR standards
• Basic Auth - Username/password authentication
• Custom - Extensible for additional auth methods

For complete security documentation, see Security Guide:

• HIPAA compliance checklist
• OAuth configuration for EPIC/Cerner/GCP
• Kubernetes security manifests
• Credential management procedures
• Audit logging implementation

MCP Apps (Interactive UIs)

LangCare MCP FHIR ships with built-in MCP Apps — interactive, rich UI views that run directly inside MCP-capable hosts like Claude Desktop. Unlike traditional chat-based tool output, MCP Apps render full React-based interfaces with charts, tables, and interactive controls while using the same underlying FHIR tools.

How it works: Each app is a single-file HTML bundle (React + TypeScript, compiled with Vite) that gets embedded into the Go binary at compile time via go:embed. At runtime the MCP server registers each app as both an MCP Resource (text/html;profile=mcp-app) and a dedicated MCP Tool linked via _meta.ui.resourceUri. When an MCP host calls the tool, it fetches the resource and renders the UI. The app calls back into the server's generic FHIR tools (fhir_search, fhir_read, etc.) through app.callServerTool() — no LLM round-trips for data fetching.

Advantages over plain tool output:

• Rich visualization — SVG charts, color-coded cards, expandable detail panels
• Interactive controls — search fields, date range pickers, click-to-expand rows
• Deterministic data fetching — apps call FHIR tools directly, no LLM involvement in data retrieval
• Zero external dependencies — everything inlines into a single HTML file, embedded in the binary
• Works offline — no CDN, no external scripts, no network requests beyond FHIR API calls

Built-in Apps

Both apps are reference implementations demonstrating the MCP Apps pattern. See apps/README.md for architecture details and how to build new apps.

Patient Chart Review running inside Claude Desktop

Agent Usage

AI agents use LangCare MCP FHIR Server to help healthcare professionals access and manage patient health records through 4 FHIR tools. The server handles EMR authentication, allowing agents to focus on clinical workflows while maintaining strict privacy and accuracy standards.

Agent capabilities:

• Search, Read, Create, Update - Any FHIR R4 resource (Patient, Observation, Medication, etc.)
• Patient privacy - Use partial identifiers, confirm identity before updates
• Clinical accuracy - Verify data, use standard codes (LOINC, SNOMED, RxNorm)
• Professional communication - Structure responses with context, findings, and next steps

Common workflows:

• Patient lookup: Search by name/DOB → verify identity → read full details
• Clinical review: Retrieve labs, vitals, medications → present with reference ranges
• Documentation: Extract structured data → map to FHIR resources → confirm → create
• Updates: Verify existing resource → modify → confirm changes → update

System support:

• Works with any FHIR R4 resource type (60+ types including DocumentReference, Binary, Media)
• Automatic authentication and token refresh to EPIC, Cerner, GCP Healthcare API
• HIPAA-compliant PHI handling with audit logging
• Comprehensive OAuth2 scopes for clinical data access

📖 Complete guide: Agent Prompt Guide - System prompt, tool examples, workflows, and error handling

Clinical Skills Library (Optional)

40+ agent-agnostic clinical workflow guides that teach AI agents how to perform complex healthcare tasks using the MCP server's 4 FHIR tools (fhir_search, fhir_read, fhir_create, fhir_update).

• Optional - The MCP server works without them
• Portable - Work with Claude, ChatGPT, Gemini, or any AI agent
• Evidence-based - Built on USPSTF, ADA, ACC/AHA, CDC, ACOG, KDIGO, and other society guidelines
• Copy-paste ready - Add a skill's SKILL.md to your agent's system prompt or custom instructions

Skill Categories (40 Skills)

Full catalog with links: skills/README.md

How to Use Skills

1. Browse the skills/core/ directory and pick a skill.
2. Copy the skill's SKILL.md content into your AI agent's system prompt or custom instructions.
3. Reference files in each skill's references/ subdirectory contain detailed clinical knowledge (scoring criteria, code tables, thresholds) that can optionally be included for deeper clinical accuracy.

# Example: Add medication-reconciliation skill to your agent
skills/core/medication-management/medication-reconciliation/
├── SKILL.md              # Copy this into agent instructions
└── references/
    ├── reconciliation-process.md   # Joint Commission standards
    └── high-risk-medications.md    # ISMP high-alert drug list

Integration guides: Claude | ChatGPT | Gemini

Community contributions welcome - see CONTRIBUTING.md for guidelines.

Development & Testing

Build from Source

make build

Run Locally (stdio mode)

make run
# or
./bin/langcare-mcp-fhir -config configs/config.local.yaml

Run in HTTP Mode (Streamable HTTP)

make run-http
# or
./bin/langcare-mcp-fhir -http -port 8080 -config configs/config.yaml

Starts the server with Streamable HTTP transport on /mcp and health check on /health.

Run Tests

make test

Lint Code

make lint

Deploy to Fly.io (Remote Streamable HTTP)

Deploy as a remote MCP server with Streamable HTTP transport, accessible by any MCP-compatible AI agent from anywhere.

# Install Fly CLI
brew install flyctl
fly auth login

# Create app
fly apps create --name langcare-mcp-dev

# Set CONFIG_FILE in fly/fly.dev.toml [env] block for your provider (EPIC or GCP)
# Then set secrets (EPIC example):
fly secrets set \
  EPIC_BASE_URL="https://fhir.epic.com/interconnect-fhir-oauth/api/FHIR/R4" \
  EPIC_CLIENT_ID="your-client-id" \
  EPIC_TOKEN_URL="https://fhir.epic.com/interconnect-fhir-oauth/oauth2/token" \
  EPIC_PRIVATE_KEY_B64="$(base64 < keys/epic/private-key.pem)" \
  MCP_AUTH_TOKENS="your-token" \
  --app langcare-mcp-dev

# Deploy
fly deploy -c fly/fly.dev.toml --app langcare-mcp-dev

# Verify
curl https://langcare-mcp-dev.fly.dev/health

Connect any MCP client to:

URL:   https://langcare-mcp-dev.fly.dev/mcp
Auth:  Authorization: Bearer your-token

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "langcare-fhir": {
      "url": "https://langcare-mcp-dev.fly.dev/mcp",
      "headers": {
        "Authorization": "Bearer your-token"
      }
    }
  }
}

Supports EPIC and GCP Healthcare API providers. See fly/README.md for provider setup, secrets, and full deployment guide.

Local Testing with EPIC

For step-by-step instructions on setting up EPIC credentials and testing locally: 📖 Local Testing Guide This guide covers:

• Generating RSA keys and JWKS
• Configuring EPIC credentials
• Running the server locally
• Testing with Claude Desktop
• Troubleshooting common issues

Quick credential test:

# Test your EPIC credentials before running the server
go run test/test_epic_token.go "your-client-id" "/path/to/private-key.pem"

Project Structure

langcare-mcp-fhir/
├── cmd/
│   └── server/
│       └── main.go                          # Entry point
├── internal/
│   ├── apps/                                # MCP Apps (embedded UIs)
│   │   ├── embed.go                         # go:embed directive for HTML bundles
│   │   ├── registry.go                      # App metadata, tool names, resource URIs
│   │   └── dist/                            # Built HTML bundles (copied by build)
│   │       ├── fhir-explorer.html           # FHIR Explorer single-file bundle
│   │       └── patient-chart-review.html    # Patient Chart Review single-file bundle
│   ├── audit/
│   │   └── logger.go                        # HIPAA audit logging
│   ├── config/
│   │   └── config.go                        # YAML configuration loading
│   ├── fhir/
│   │   ├── client.go                        # FHIR HTTP client interface
│   │   ├── types.go                         # FHIR client types
│   │   └── providers/                       # Backend implementations
│   │       ├── base.go                      # Base HTTP provider
│   │       ├── epic.go                      # EPIC OAuth2 provider
│   │       ├── cerner.go                    # Cerner OAuth2 provider
│   │       └── gcp.go                       # GCP Healthcare API provider
│   ├── mcp/
│   │   └── server.go                        # MCP server + app registration
│   ├── middleware/
│   │   ├── auth.go                          # MCP authentication
│   │   └── rate_limit.go                    # Rate limiting
│   ├── tools/                               # MCP tool implementations
│   │   ├── registry.go                      # Tool registry
│   │   ├── fhir_read.go                     # Read FHIR resource
│   │   ├── fhir_search.go                   # Search FHIR resources
│   │   ├── fhir_create.go                   # Create FHIR resource
│   │   └── fhir_update.go                   # Update FHIR resource
│   └── transport/
│       ├── stdio.go                         # stdio transport (Claude Desktop)
│       └── http.go                          # Streamable HTTP transport (production)
├── apps/                                    # MCP App source code (React + TypeScript)
│   ├── README.md                            # App development guide
│   ├── package.json                         # Shared dependencies (React 19, MCP Apps SDK)
│   ├── vite.config.ts                       # Vite build config (single-file output)
│   ├── tsconfig.json                        # TypeScript config
│   ├── fhir-explorer/                       # FHIR Explorer app
│   │   ├── index.html
│   │   └── src/
│   │       ├── app.tsx
│   │       └── global.css
│   └── patient-chart-review/                # Patient Chart Review app
│       ├── index.html
│       └── src/
│           ├── app.tsx
│           └── global.css
├── scripts/
│   ├── build-apps.sh                        # Build all apps → internal/apps/dist/
│   └── create_jwks.sh                       # Generate JWKS from public key (EPIC)
├── pkg/
│   └── types/
│       └── errors.go                        # Custom error types
├── configs/
│   ├── config.epic.example.yaml             # Example configuration for EPIC
│   ├── config.cerner.example.yaml           # Example configuration for Cerner
│   ├── config.gcp.example.yaml              # Example configuration for GCP
│   └── config.base.example.yaml             # Example configuration for any FHIR R4 server
├── docs/
│   ├── AGENT_PROMPT.md                      # AI agent system prompt
│   ├── EPIC-APP-SECURITY.md                 # EPIC authentication setup
│   ├── EPIC-SCOPES.md                       # OAuth2 scopes reference
│   ├── LOCAL-TESTING.md                     # Local development guide
│   └── SECURITY.md                          # Production security guide
├── test/
│   ├── README.md                            # Test documentation
│   └── test_epic_token.go                   # EPIC OAuth2 token tester
├── fly/
│   ├── Dockerfile                           # Multi-stage Go build for Fly.io
│   ├── docker-entrypoint.sh                 # Key materialization + server startup
│   ├── fly.dev.toml                         # Fly.io dev deployment config
│   ├── config.fly.epic.yaml                 # Fly.io EPIC provider config
│   ├── config.fly.gcp.yaml                  # Fly.io GCP provider config
│   └── README.md                            # Fly.io deployment guide
├── bin/                                     # Build output (gitignored)
│   └── langcare-mcp-fhir                    # Compiled binary
├── go.mod                                   # Go module definition
├── go.sum                                   # Go module checksums
├── Makefile                                 # Build commands
└── README.md                                # This file

Note: The following are gitignored and not committed:

• keys/ - Private keys and credentials
• config.local.*.yaml - Local configuration files
• bin/ - Compiled binaries
• .env - Environment variables
• apps/node_modules/, apps/dist/, apps/dist-tmp/ - App build artifacts

Healthcare Voice Agent

Real-time voice AI that lets patients ask about their health records and get spoken answers pulled directly from their EMR.

The stack: PipeCat (open-source, Daily.co) for the voice pipeline — STT, LLM orchestration, TTS with sub-3-second latency. Claude for clinical reasoning and tool calling. LangCare MCP FHIR Server (open-source, Go) as a stateless proxy to any FHIR R4 EMR — Epic, Cerner, GCP Healthcare API.

MCP is the glue. PipeCat's native MCP client auto-discovers FHIR tools at startup. Patient asks "What medications am I on?" — Claude calls fhir_search — PipeCat routes it to the MCP server — data comes back — Claude responds in natural speech. No manual tool schemas needed.

Three-layer HIPAA auth: Caller identity verified before the session starts, bearer token to MCP, OAuth2/SMART on FHIR to EMR. Zero PHI storage.

Everything is swappable. Replace Claude with Gemini, DeepGram with Google STT, Daily with WebSocket. The MCP FHIR layer and clinical prompts stay the same.

Full documentation and setup guide

LangCare CLI

Command-line interface that wraps the 4 FHIR MCP tools (fhir_search, fhir_read, fhir_create, fhir_update) as CLI subcommands over HTTP. Built for AI agent frameworks that don't speak MCP natively — LangChain, smolagents, CrewAI, AutoGen, and any framework that can call a subprocess. The CLI handles the MCP session handshake internally, so agents get clean JSON on stdout with no protocol knowledge required.

# Install
pip install "langcare-cli @ git+https://github.com/langcare/langcare-mcp-fhir.git#subdirectory=cli"

# Use
langcare fhir search Patient --query "name=John"
langcare fhir read Patient 123
langcare fhir create Observation --data @obs.json
langcare fhir update Patient 123 --data @patient.json

The 40+ clinical skills in the Skills Library work as-is — skills reference abstract tool names, not transport. Register the CLI as subprocess tools in your agent framework and skills run without modification.

Full documentation and setup guide

📄 License

See LICENSE file.

Built with ❤️ by the LangCare team and contributors.

Improving healthcare through better AI infrastructure.