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

Related MCP server: MOF Tools MCP Server

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:

   python -m venv venv
   source venv/bin/activate  # On Unix/macOS

3. Install dependencies using Poetry:

   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]