Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@healthcare-mcp-demo Update patient P-101 status to Discharged"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A lightweight healthcare demo application showcasing the latest stateless Model Context Protocol (MCP) standard using FastMCP, Pydantic AI, and Google Gemini.
This repository demonstrates how to build a secure, server-side MCP infrastructure that exposes clinical read endpoints, state mutation tools, and workflow prompts, then orchestrates them via an AI Host layer.
MCP Resources (Read Operations): Exposes passive context via custom URIs (e.g., healthcare://patients/{patient_id}/record). Used by the AI model to fetch factual data without side effects.
MCP Tools (Write/Mutation Operations): Exposes executable actions (e.g., update_patient_status). Converts Python type hints and docstrings into JSON Schemas for AI function calling.
MCP Prompts (Workflow Templates): Centralizes clinical prompt logic on the server (e.g., generate_discharge_summary_prompt), providing standardized instructions across all client environments.
Transports (HTTP/SSE): Runs as a stateless remote server over Server-Sent Events (SSE) on HTTP port 8000.
Authentication: Uses Bearer Token authorization to secure remote SSE server connections.
AI Host Integration: Uses pydantic-ai and google-gla:gemini-2.5-flash to automatically inspect tools, make clinical decisions, and execute mutations via natural language queries.
Related MCP server: atlas_mcp
healthcare-mcp-demo/
├── server.py # FastMCP Server (Resources, Tools, Prompts, SSE transport)
├── client.py # Deterministic host test script (Direct MCP protocol verification)
├── pydantic_llm_client.py # AI Host script (Pydantic AI + Gemini + MCPToolset + Auth)
├── pyproject.toml # Project dependencies managed by uv
├── uv.lock # Lockfile for precise dependency resolution
└── README.md # Documentation
----
Prerequisites
Python: 3.10 or higher
Package Manager: uv installed on your system
API Key: Google Gemini API key (GEMINI_API_KEY)
Installation & Setup
Clone or navigate to the repository:
PowerShell
cd healthcare-mcp-demo
Install Dependencies:
uv will automatically set up the virtual environment and install all necessary packages:
PowerShell
uv sync
Configure Environment Variables:
Set your Gemini API key in your terminal session:
PowerShell
# Windows PowerShell
$env:GEMINI_API_KEY="your-actual-gemini-api-key"
# Linux / macOS
export GEMINI_API_KEY="your-actual-gemini-api-key"
Running the Application
Running the demo requires two terminal windows:
Step 1: Start the Remote MCP Server (Terminal 1)
Run the server script using uv:
PowerShell
uv run .\server.py
The server will start listening on http://0.0.0.0:8000/sse.
Step 2: Run the AI Host Client (Terminal 2)
In a second terminal, execute the Pydantic AI client script:
PowerShell
uv run .\pydantic_llm_client.py
Expected Output Workflow
The client establishes an authenticated SSE connection using a Bearer Token (secure-healthcare-secret-token-123).
Pydantic AI sends the user query alongside the discovered MCP tools to Gemini.
Gemini determines that update_patient_status needs to be called.
The MCP Tool executes on server.py, updating the record for patient P-101.
Gemini synthesizes the execution feedback and returns a natural language response:
Plaintext
User Request: 'Please update patient P-101's status to 'Discharged' and set condition to 'Acute Bronchitis - Fully Recovered'.'
Executing request via Gemini + FastMCP...
=== GEMINI RESPONSE ===
The patient record for P-101 has been successfully updated.
* Status: Discharged
* Condition: Acute Bronchitis - Fully RecoveredThis server cannot be deployed
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