---
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

```bash
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:

```bash
# 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:

```bash
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`:

```bash
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.