Lingshu FastMCP Medical AI Service
Enables medical image analysis, structured medical report generation, and medical Q\&A through the Lingshu medical AI model. Provides healthcare professionals and developers with AI-powered medical assistance capabilities via a FastMCP server interface.
README
Lingshu FastMCP Medical AI Service
This project implements a FastMCP server for the Lingshu medical AI model and a corresponding client for testing and integration.
Components
mcp_server_lingshu.py: FastMCP server wrapping the Lingshu modelmcp_client_lingshu.py: Test client demonstrating interaction with the Lingshu FastMCP server
Server Features
- Medical image analysis
- Structured medical report generation
- Medical Q&A
Prerequisites
- FastMCP framework
- OpenAI API compatible LLM server (e.g., vLLM)
- Required Python packages (install via
pip install -r requirements.txt)
Setup
- Clone the repository
- Install dependencies:
pip install -r requirements.txt
Usage
Use vLLM to serve the Lingshu Model
vllm serve lingshu-medical-mllm/Lingshu-7B --dtype float16 --api_key api_key --port 8000 --max-model-len 32768
Wrap the server with FastMCP
export LINGSHU_SERVER_URL="http://localhost:8000/v1"
export LINGSHU_SERVER_API="api_key"
export LINGSHU_MODEL="lingshu-medical-mllm/Lingshu-7B" # the above config depends on your vllm server config
python mcp_server_lingshu.py --host 127.0.0.1 --port 4200 --path /lingshu --log-level info
Try connecting Lingshu with MCP
export LLM_SERVER_URL="xxx"
export LLM_SERVER_API="xxx"
export LLM_MODEL="xxx" ## this is your own model
python mcp_client_lingshu.py --mcp-url http://127.0.0.1:4200/lingshu # the mcp-url should depend on the mcp server you deployed in the last step
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.