🏥 MCP Server for AWS HealthLake (Mocked)
Description
# 🏥 MCP Server for AWS HealthLake (Mocked) This project is an **open-source Model Context Protocol (MCP) server** that connects to (mocked) AWS HealthLake data sources and provides structured, standardized context bundles for clinical models. ## ✅ Features - Mocked AWS…
About
# 🏥 MCP Server for AWS HealthLake (Mocked) This project is an **open-source Model Context Protocol (MCP) server** that connects to (mocked) AWS HealthLake data sources and provides structured, standardized context bundles for clinical models. ## ✅ Features - Mocked AWS HealthLake APIs (FHIR-based) - Context bundling…
Details
- Author
- anandkiran2007
- Downloads
- 312
- Categories
- Cloud Service
Jump to
- Mocked AWS HealthLake APIs based on FHIR
- Context bundling per MCP spec (FHIR + NLP)
- Model registration and tailored context delivery
- Mocked model invocation endpoint
- Audit logging
- Optional USE_AWS flag for future AWS integration
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
🏥 MCP Server for AWS HealthLake (Mocked)Command (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, create a Python virtual environment, install dependencies with pip install -r requirements.txt, and run the server locally with uvicorn app.main:app --reload. By default the server runs in mock mode; set the environment variable USE_AWS=true to prepare for future AWS integration (AWS mode is not yet implemented). Access the interactive documentation at http://localhost:8000/docs.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"\ud83c\udfe5 mcp server for aws healthlake (mocked)": {
"ahl-mcp-server": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"ahl-mcp-server": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}🏥 MCP Server for AWS HealthLake (Mocked)
This project is an open-source Model Context Protocol (MCP) server that connects to (mocked) AWS HealthLake data sources and provides structured, standardized context bundles for clinical models.
✅ Features
- Mocked AWS HealthLake APIs (FHIR-based)
- Context bundling per MCP spec (FHIR + NLP)
- Model registration and tailored context delivery
- Mocked model invocation endpoint
- Audit logging
- Optional USE_AWS flag for future AWS integration
---
🚀 Quick Start
1. Clone or Download
unzip mcp-server.zip
cd ahl-mcp-server
2. Install Python Dependencies
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
3. Run Locally (Mock Mode)
uvicorn app.main:app --reload
Visit: http://localhost:8000/docs
---
🔁 Endpoints
📦 MCP Endpoints
| Endpoint | Method | Description |
|----------|--------|-------------|
| /context/{patient_id} | GET | Get context bundle |
| /models/register | POST | Register model info |
| /models/{model_id}/context/{patient_id} | GET | Tailored context |
| /model/invoke/{model_id}?patient_id=... | POST | Mock model prediction |
🧪 Mock HealthLake Endpoints
| Endpoint | Method | Description |
|----------|--------|-------------|
| /ahl/Patient | POST | Create patient |
| /ahl/Patient/{id} | GET | Get patient |
| /ahl/Condition?patient=... | GET | Get conditions |
| /ahl/MedicationRequest?patient=... | GET | Get meds |
| /ahl/Observation?patient=... | GET | Get labs |
| /ahl/mock/add_* | POST | Add mock data (condition/med/obs) |
---
⚙️ Switching Between Mock and AWS Mode
By default, the app runs in mock mode.
To prepare for future AWS integration:
export USE_AWS=true
Note: AWS mode is not implemented yet but ready for integration.
---
🧪 Run Tests
pytest tests/
---
📂 Folder Structure
app/
├── api/ # API endpoints
├── core/ # Config and logging
├── fhir/ # Placeholder for FHIR connector
├── models/ # Pydantic models
├── nlp/ # NLP mock enhancer
├── services/ # Context resolver, policy logic
├── main.py # FastAPI entry
infra/
tests/ # Test cases
schemas/ # MCP JSON schema
.env
requirements.txt
---
📜 License
MIT License
---
🧠 Coming Soon
- AWS HealthLake integration (boto3)
- Amazon Comprehend Medical / Bedrock models
- Dockerfile + ECS deployment
- Real-time cohort search
- SMART-on-FHIR authentication
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