---
license: mit
---

# Bio-MCP-Data

A repository containing biological datasets that will be used by BIO-MCP MCP (Model Context Protocol) standard.

## About

This repository hosts biological data assets formatted to be compatible with the Model Context Protocol, enabling AI models to efficiently access and process biological information. The data is managed using Git Large File Storage (LFS) to handle large biological datasets.

## Purpose

- Provide standardized biological datasets for AI model training and inference
- Enable seamless integration with MCP-compatible AI agents
- Support research in computational biology, genomics, and bioinformatics

## Usage

To clone this repository including the LFS data:

```bash
git lfs install
git clone https://huggingface.co/datasets/longevity-genie/bio-mcp-data
```

Alternatively, you can clone using SSH:

```bash
git lfs install
git clone git@hf.co:datasets/longevity-genie/bio-mcp-data
```

## Data Format

The datasets in this repository store datasets used by our MCP server which enable structured data access for AI systems. This standardization facilitates:

- Consistent data integration across different AI models
- Improved context handling for biological information
- Enhanced tool access for AI agents working with biological data

## Contributing

Contributions to this dataset collection are welcome. Please follow standard Git LFS practices when adding new biological data files.

## Using the Data in Your Project

To use the datasets in this repository with your Python projects, you can leverage the `huggingface-hub` library for easy downloading and caching of specific files, and `uv` for managing your project dependencies.

### 1. Setting up your environment with `uv`

First, ensure you have `uv` installed. If not, you can install it following the official `uv` installation guide (e.g., `pip install uv` or other methods recommended by astral.sh).

Then, create and activate a virtual environment for your project:
```bash
uv venv my_project_env  # Creates a virtual environment named my_project_env
source my_project_env/bin/activate # On Linux/macOS
# my_project_env\Scripts\activate # On Windows
```

Install the `huggingface-hub` library into your activated environment:
```bash
uv pip install huggingface-hub
```
If you plan to work with common data formats, you might also want to install libraries like `pandas`:
```bash
uv pip install pandas
```

### 2. Accessing data files with `huggingface-hub`

You can download individual files from this Hugging Face Dataset repository using the `hf_hub_download` function from the `huggingface-hub` library. This function handles the download and caches the file locally for future use.

**Example:**

Let's assume you want to download a specific data file, for instance, `hagr/anage_data.csv` from this repository.

```python
from huggingface_hub import hf_hub_download
import os # Optional: for constructing paths

# Define the repository ID and the file path within the repository
repo_id = "longevity-genie/bio-mcp-data"  # This is the Hugging Face Hub ID for this dataset
# IMPORTANT: Replace "hagr/anage_data.csv" with the actual path to a file in this repository.
# You can browse the files on the Hugging Face Hub page for this dataset to find correct paths.
file_to_download = "hagr/anage_data.csv"

try:
    # Download the file
    downloaded_file_path = hf_hub_download(
        repo_id=repo_id,
        filename=file_to_download,
        repo_type="dataset"  # Crucial for dataset repositories
    )
    
    print(f"File '{file_to_download}' downloaded successfully to: {downloaded_file_path}")

    # Now you can use the downloaded_file_path to load and process the data.
    # For example, if it's a CSV file and you have pandas installed:
    import pandas as pd
    try:
        data_frame = pd.read_csv(downloaded_file_path)
        print("Successfully loaded data into pandas DataFrame:")
        print(data_frame.head())
    except Exception as e_pandas:
        print(f"Could not load CSV with pandas: {e_pandas}")

except Exception as e:
    print(f"An error occurred while trying to download '{file_to_download}': {e}")
    print(f"Please ensure the file path is correct and the file exists in the repository '{repo_id}'.")
    print("You can check available files at: https://huggingface.co/datasets/longevity-genie/bio-mcp-data/tree/main")

```

**Explanation:**

-   **`repo_id`**: This is the identifier of the dataset on Hugging Face Hub (`longevity-genie/bio-mcp-data`).
-   **`filename`**: This is the relative path to the file *within* the repository (e.g., `data/some_file.fasta`, `annotations/genes.gff`). You need to replace `"hagr/anage_data.csv"` with the actual path to a file you intend to use.
-   **`repo_type="dataset"`**: This tells `huggingface-hub` that you are targeting a dataset repository.
-   The `hf_hub_download` function returns the local path to the downloaded (and cached) file. You can then use this path with any Python library that can read files (like `pandas` for CSVs, `BioPython` for sequence files, etc.).

This method is efficient as it only downloads the files you specifically request and utilizes local caching, saving time and bandwidth on subsequent runs.

## License

This project is licensed under the MIT License - see the license information above for details.