awslabs.healthlake-mcp-server 0.0.1

An AWS Labs Model Context Protocol (MCP) server for healthlake

HealthLake MCP Server

A Model Context Protocol (MCP) server for AWS HealthLake FHIR operations. Provides 11 tools for comprehensive FHIR resource management with automatic datastore discovery.

Table of Contents

• Features
• Prerequisites
• Quick Start
  • Option 1: uvx (Recommended)
  • Option 2: uv install
  • Option 3: Docker
• MCP Client Configuration
  • Amazon Q Developer CLI
  • Docker Configuration
  • Other MCP Clients
• Read-Only Mode
• Available Tools
  • Datastore Management
  • FHIR Resource Operations (CRUD)
  • Advanced Search
  • Job Management
  • MCP Resources
• Usage Examples
  • Basic Resource Operations
  • Advanced Search
  • Patient Everything
• Authentication
  • Required Permissions
• Error Handling
• Troubleshooting
  • Common Issues
  • Debug Mode
• Development
  • Local Development Setup
  • Running the Server Locally
  • Development Workflow
  • IDE Setup
  • Testing
  • Project Structure
• Contributing
• License
• Support

Features

• 11 FHIR Tools: Complete CRUD operations (6 read-only, 5 write), advanced search, patient-everything, job management
• Read-Only Mode: Security-focused mode that blocks all mutating operations while preserving read access
• MCP Resources: Automatic datastore discovery - no manual datastore IDs needed
• Advanced Search: Chained parameters, includes, revIncludes, modifiers, and date/number prefixes with pagination
• AWS Integration: SigV4 authentication with automatic credential handling and region support
• Comprehensive Testing: 235 tests with 96% coverage ensuring reliability
• Task Automation: Poethepoet integration for streamlined development workflow
• Error Handling: Structured error responses with specific error types and helpful messages
• Docker Support: Containerized deployment with flexible authentication options

Prerequisites

• Python 3.10+ (required by MCP framework)
• AWS credentials configured
• AWS HealthLake access with appropriate permissions

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Quick Start

Choose your preferred installation method:

Cursor	VS Code

Option 1: uvx (Recommended)

# Install and run latest version automatically
uvx awslabs.healthlake-mcp-server@latest

Option 2: uv install

uv tool install awslabs.healthlake-mcp-server
awslabs.healthlake-mcp-server

Option 3: Docker

# Build and run with Docker
docker build -t healthlake-mcp-server .
docker run -e AWS_ACCESS_KEY_ID=xxx -e AWS_SECRET_ACCESS_KEY=yyy healthlake-mcp-server

# Or use pre-built image with environment variables
docker run -e AWS_ACCESS_KEY_ID=your_key -e AWS_SECRET_ACCESS_KEY=your_secret -e AWS_REGION=us-east-1 awslabs/healthlake-mcp-server

# With AWS profile (mount credentials)
docker run -v ~/.aws:/root/.aws -e AWS_PROFILE=your-profile awslabs/healthlake-mcp-server

# Read-only mode
docker run -e AWS_ACCESS_KEY_ID=your_key -e AWS_SECRET_ACCESS_KEY=your_secret -e AWS_REGION=us-east-1 awslabs/healthlake-mcp-server --readonly

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MCP Client Configuration

Amazon Q Developer CLI

Add to your MCP configuration file:

Location:

• macOS: ~/.aws/amazonq/mcp.json
• Linux: ~/.config/amazon-q/mcp.json
• Windows: %APPDATA%\Amazon Q\mcp.json

Configuration:

{
  "mcpServers": {
    "healthlake": {
      "command": "uvx",
      "args": ["awslabs.healthlake-mcp-server@latest"],
      "env": {
        "AWS_REGION": "us-east-1",
        "AWS_PROFILE": "your-profile-name",
        "MCP_LOG_LEVEL": "INFO"
      }
    }
  }
}

Read-Only Configuration:

{
  "mcpServers": {
    "healthlake-readonly": {
      "command": "uvx",
      "args": ["awslabs.healthlake-mcp-server@latest", "--readonly"],
      "env": {
        "AWS_REGION": "us-east-1",
        "AWS_PROFILE": "your-profile-name",
        "MCP_LOG_LEVEL": "INFO"
      }
    }
  }
}

Docker Configuration

With environment variables:

{
  "mcpServers": {
    "healthlake": {
      "command": "docker",
      "args": [
        "run", "--rm",
        "-e", "AWS_ACCESS_KEY_ID=your_key",
        "-e", "AWS_SECRET_ACCESS_KEY=your_secret",
        "-e", "AWS_REGION=us-east-1",
        "-e", "MCP_LOG_LEVEL=INFO",
        "awslabs/healthlake-mcp-server"
      ]
    }
  }
}

With AWS credentials mounted:

{
  "mcpServers": {
    "healthlake": {
      "command": "docker",
      "args": [
        "run", "--rm",
        "-v", "~/.aws:/root/.aws",
        "-e", "AWS_PROFILE=your-profile",
        "-e", "MCP_LOG_LEVEL=INFO",
        "awslabs/healthlake-mcp-server"
      ]
    }
  }
}

Read-Only Mode with Docker:

{
  "mcpServers": {
    "healthlake-readonly": {
      "command": "docker",
      "args": [
        "run", "--rm",
        "-e", "AWS_ACCESS_KEY_ID=your_key",
        "-e", "AWS_SECRET_ACCESS_KEY=your_secret",
        "-e", "AWS_REGION=us-east-1",
        "-e", "MCP_LOG_LEVEL=INFO",
        "awslabs/healthlake-mcp-server",
        "--readonly"
      ]
    }
  }
}

Other MCP Clients

See examples/mcp_config.json for additional configuration examples.

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Read-Only Mode

The server supports a read-only mode that prevents all mutating operations while still allowing read operations. This is useful for:

• Safety: Preventing accidental modifications in production environments
• Testing: Allowing safe exploration of FHIR resources without risk of changes
• Auditing: Running the server in environments where only read access should be allowed
• Compliance: Meeting security requirements for read-only access to healthcare data

Enabling Read-Only Mode

Add the --readonly flag when starting the server:

# Using uvx
uvx awslabs.healthlake-mcp-server@latest --readonly

# Or if installed locally
python -m awslabs.healthlake_mcp_server.main --readonly

Operations Available in Read-Only Mode

Operation	Available	Description
list_datastores	✅	List all HealthLake datastores
get_datastore_details	✅	Get detailed datastore information
read_fhir_resource	✅	Retrieve specific FHIR resources
search_fhir_resources	✅	Advanced FHIR search operations
patient_everything	✅	Comprehensive patient record retrieval
list_fhir_jobs	✅	Monitor import/export job status

Operations Blocked in Read-Only Mode

Operation	Blocked	Description
create_fhir_resource	❌	Create new FHIR resources
update_fhir_resource	❌	Update existing FHIR resources
delete_fhir_resource	❌	Delete FHIR resources
start_fhir_import_job	❌	Start FHIR data import jobs
start_fhir_export_job	❌	Start FHIR data export jobs

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Available Tools

The server provides 11 comprehensive FHIR tools organized into four categories:

Datastore Management

• list_datastores - List all HealthLake datastores with optional status filtering
• get_datastore_details - Get detailed datastore information including endpoints and metadata

FHIR Resource Operations (CRUD)

• create_fhir_resource - Create new FHIR resources with validation
• read_fhir_resource - Retrieve specific FHIR resources by ID
• update_fhir_resource - Update existing FHIR resources with versioning
• delete_fhir_resource - Delete FHIR resources from datastores

Advanced Search

• search_fhir_resources - Advanced FHIR search with modifiers, chaining, includes, and pagination
• patient_everything - Comprehensive patient record retrieval using FHIR $patient-everything operation

Job Management

• start_fhir_import_job - Start FHIR data import jobs from S3
• start_fhir_export_job - Start FHIR data export jobs to S3
• list_fhir_jobs - List and monitor import/export jobs with status filtering

MCP Resources

The server automatically exposes HealthLake datastores as MCP resources, enabling:

• Automatic discovery of available datastores
• No manual datastore ID entry required
• Status visibility (ACTIVE, CREATING, etc.)
• Metadata access (creation date, endpoints, etc.)

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Usage Examples

Basic Resource Operations

// Create a patient (datastore discovered automatically)
{
  "datastore_id": "discovered-from-resources",
  "resource_type": "Patient",
  "resource_data": {
    "resourceType": "Patient",
    "name": [{"family": "Smith", "given": ["John"]}],
    "gender": "male"
  }
}

Advanced Search

// Search with modifiers and includes
{
  "datastore_id": "discovered-from-resources",
  "resource_type": "Patient",
  "search_params": {
    "name:contains": "smith",
    "birthdate": "ge1990-01-01"
  },
  "include_params": ["Patient:general-practitioner"],
  "revinclude_params": ["Observation:subject"]
}

Patient Everything

// Get all resources for a patient
{
  "datastore_id": "discovered-from-resources",
  "patient_id": "patient-123",
  "start": "2023-01-01",
  "end": "2023-12-31"
}

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Authentication

Configure AWS credentials using any of these methods:

1. AWS CLI: aws configure
2. Environment variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY
3. IAM roles (for EC2/Lambda)
4. AWS profiles: Set AWS_PROFILE environment variable

Required Permissions

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "healthlake:ListFHIRDatastores",
        "healthlake:DescribeFHIRDatastore",
        "healthlake:CreateResource",
        "healthlake:ReadResource",
        "healthlake:UpdateResource",
        "healthlake:DeleteResource",
        "healthlake:SearchWithGet",
        "healthlake:SearchWithPost",
        "healthlake:StartFHIRImportJob",
        "healthlake:StartFHIRExportJob",
        "healthlake:ListFHIRImportJobs",
        "healthlake:ListFHIRExportJobs"
      ],
      "Resource": "*"
    }
  ]
}

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Error Handling

All tools return structured error responses:

{
  "error": true,
  "type": "validation_error",
  "message": "Datastore ID must be 32 characters"
}

Error Types:

• validation_error - Invalid input parameters
• not_found - Resource or datastore not found
• auth_error - AWS credentials not configured
• service_error - AWS HealthLake service error
• server_error - Internal server error

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Troubleshooting

Common Issues

"AWS credentials not configured"

• Run aws configure or set environment variables
• Verify AWS_REGION is set correctly

"Resource not found"

• Ensure datastore exists and is ACTIVE
• Check datastore ID is correct (32 characters)
• Verify you have access to the datastore

"Validation error"

• Check required parameters are provided
• Ensure datastore ID format is correct
• Verify count parameters are within 1-100 range

Debug Mode

Set environment variable for detailed logging:

export PYTHONPATH=.
export MCP_LOG_LEVEL=DEBUG
awslabs.healthlake-mcp-server

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Development

Local Development Setup

Option 1: Using uv (Recommended)

git clone <repository-url>
cd healthlake-mcp-server
uv sync --dev
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Option 2: Using pip/venv

git clone <repository-url>
cd healthlake-mcp-server

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -e ".[dev]"

Option 3: Using conda

git clone <repository-url>
cd healthlake-mcp-server

# Create conda environment
conda create -n healthlake-mcp python=3.10
conda activate healthlake-mcp

# Install dependencies
pip install -e ".[dev]"

Running the Server Locally

# After activating your virtual environment
python -m awslabs.healthlake_mcp_server.main

# Or using the installed script
awslabs.healthlake-mcp-server

Development Workflow

# Run tests
poe test

# Run tests with coverage
poe test-cov

# Format code
poe format

# Lint code
poe lint

# Run all quality checks
poe check

# Clean build artifacts
poe clean

# Build package
poe build

# Run server
poe run

Available Tasks

The project uses Poethepoet for task automation. Run poe --help to see all available tasks:

• Testing: test, test-cov
• Code Quality: lint, format, check, security
• Build & Run: build, run
• Cleanup: clean

Development Workflow

# Run all checks
poe check

IDE Setup

VS Code

1. Install Python extension
2. Select the virtual environment: Ctrl+Shift+P → "Python: Select Interpreter"
3. Choose .venv/bin/python

PyCharm

1. File → Settings → Project → Python Interpreter
2. Add Interpreter → Existing Environment
3. Select .venv/bin/python

Testing

# Run unit tests (fast, no AWS dependencies)
poe test

# Run with coverage
poe test-cov

# Format code
poe format

# Lint code
poe lint

Test Results: 235 tests pass, 96% coverage

Project Structure

awslabs/healthlake_mcp_server/
├── server.py           # MCP server with tool handlers
├── fhir_operations.py  # AWS HealthLake client operations
├── models.py          # Pydantic validation models
├── main.py            # Entry point
└── __init__.py        # Package initialization

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Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make changes and add tests
4. Run tests: poe test
5. Format code: poe format
6. Submit a pull request

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License

Licensed under the Apache License, Version 2.0. See LICENSE file for details.

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Support

For issues and questions:

• Check the troubleshooting section above
• Review AWS HealthLake documentation
• Open an issue in the repository

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