# Materials MCP Project

A Model Context Protocol (MCP) server designed to interact with materials databases through the OPTIMADE API, with a specific focus on Google DeepMind's GNoME (Graph Networks for Materials Exploration) dataset. This project serves as a bridge between the OPTIMADE API and materials science applications, enabling efficient access and manipulation of crystal structure data.

## Overview

The Materials MCP Project implements a Model Context Protocol server that:
- Interfaces with the OPTIMADE API to access materials databases
- Provides specialized access to the GNoME dataset, which contains millions of predicted stable crystal structures
- Enables efficient querying and retrieval of crystal structures and their properties
- Supports standardized data exchange formats for materials science applications

## Features

- OPTIMADE API integration for standardized materials database access
- GNoME dataset integration for accessing predicted stable crystal structures
- RESTful API endpoints for crystal structure queries
- Support for common materials science data formats
- Efficient data caching and retrieval mechanisms
- Standardized query language support

## Setup

1. Ensure you have Python 3.10 or higher installed
2. Create a virtual environment:
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Unix/macOS
   ```
3. Install dependencies using Poetry:
   ```bash
   pip install poetry
   poetry install
   ```

## Project Structure

- `materials_mcp/` - Main package directory
  - `api/` - OPTIMADE API integration
  - `gnome/` - GNoME dataset specific functionality
  - `models/` - Data models and schemas
  - `server/` - MCP server implementation
- `tests/` - Test directory
- `pyproject.toml` - Project configuration and dependencies
- `README.md` - This file

## Dependencies

- Python >=3.10
- optimade >=1.2.4 - For OPTIMADE API integration
- Additional dependencies will be added as needed for:
  - FastAPI/Flask for the web server
  - Database integration
  - Data processing and analysis
  - Testing and documentation

## Usage

[Usage examples will be added as the project develops]

## Contributing

[Contribution guidelines will be added]

## License

[License information will be added]

## Acknowledgments

- Google DeepMind for the GNoME dataset
- OPTIMADE consortium for the API specification
- [Other acknowledgments to be added]