Python Medical MCP Server
@HeyBatlle1
About Python Medical MCP Server
this is a cloned version of
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"medical-mcp-server": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}
}Tools
No tools detected
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Overview
What is Python Medical MCP Server?
A Python-based server that provides a dual interface – direct Model Context Protocol (MCP) and an HTTP REST API (FastAPI) – for performing trauma assessments. It uses a shared core medical logic module and is designed for medical assessment applications.
How to use Python Medical MCP Server?
Clone the repository, create a Python virtual environment, install dependencies from requirements.txt, then run two processes: your existing MCP script and api_server.py. You can manage them manually or with PM2. Example commands: pm2 start your_original_mcp_script.py --name "python-mcp-direct" --interpreter python3 and pm2 start "uvicorn api_server:app --host 0.0.0.0 --port 8002" --name "python-mcp-http-api".
Key features of Python Medical MCP Server
- Shared medical logic centralized in
medical_core.py - FastAPI HTTP server with RESTful endpoints
- CORS enabled for frontend applications
- Pydantic validation for request and response
- PM2 ready for process management
Use cases of Python Medical MCP Server
- Performing trauma assessments via a direct MCP client
- Integrating trauma assessment into web applications (e.g., Bolt.new) through the HTTP API
- Deploying a medical assessment service usable by both AI agents and web frontends
FAQ from Python Medical MCP Server
What interfaces does the Python Medical MCP Server provide?
It provides a direct MCP interface (via your original script) and an HTTP REST API interface (via FastAPI in api_server.py).
What are the main API endpoints?
GET /api/status returns the server status; POST /api/medical/trauma-assessment performs a trauma assessment and returns severity, immediate actions, and red flags.
How do I install and run the server?
Clone the repo, create a virtual environment, run pip install -r requirements.txt, then start the MCP script and the API server either manually or with PM2.
What dependencies are required?
Python dependencies listed in requirements.txt, including FastAPI, uvicorn, and Pydantic. PM2 (Node.js) is optional for process management
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