README.md

metadata

title: BioMedNorm MCP Server
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: true
license: apache-2.0
python_version: 3.13.3
tags:
  - mcp-server-track

BioMedNorm MCP Server

A MCP server for extracting and normalizing domain-specific entities from biomedical text. We leverage OpenAI LLMs to identify entities and match them to standardized terminology.

Installation

This project uses uv from Astral for dependency management. Follow these steps to set up the project:

Clone the repository

git clone https://github.com/yourusername/entity-extraction-mcp
cd entity-extraction-mcp

Set up Python environment

The project includes a .python-version file that specifies the required Python version. Make sure you have uv installed:

# Install uv if you don't have it already
curl -LsSf https://astral.sh/uv/install.sh | sh

Install dependencies

The project dependencies are defined in pyproject.toml. Install them with:

uv pip install -e .

Set up environment variables

The project requires an OpenAI API key, which should be stored in a .env file.

Running the application

Run the application using uv run:

uv run app.py

This command ensures that:

• All project dependencies are correctly installed
• The environment variables from .env are loaded
• The application runs in the proper environment

After starting the server, you can access:

• Web interface: http://your-server:port
• MCP endpoint: http://your-server:port/gradio_api/mcp/sse

Using the Web Interface

• Enter text in the input area
• Select the entity type (Disease, Tissue, or Cell Type)
• Click "Normalize"
• View the normalized entities in the results area

Using as an MCP Tool

The server exposes an MCP-compatible endpoint that can be used by AI agents. The tool accepts:

• paragraph: Text to extract entities from
• target_entity: Type of entity to extract ("Disease", "Tissue", or "Cell Type")

and returns a list of normalized entities.