mcp_output/analysis.json

{
    "project_name": "MatDeepLearn",
    "project_description": "A platform for testing and using graph neural networks (GNNs) for materials chemistry applications",
    "repository": "https://github.com/Fung-Lab/MatDeepLearn",
    "mcp_tools": [
        {
            "name": "check_environment",
            "description": "Check if MatDeepLearn environment is properly configured and GPU is available"
        },
        {
            "name": "list_available_models",
            "description": "List all available GNN models in MatDeepLearn"
        },
        {
            "name": "get_model_config",
            "description": "Get the default configuration for a specific model"
        },
        {
            "name": "process_structure_data",
            "description": "Process atomic structure data into graph format for GNN training"
        },
        {
            "name": "train_model",
            "description": "Train a GNN model on processed structure data"
        },
        {
            "name": "predict_properties",
            "description": "Use a trained model to predict properties of new structures"
        },
        {
            "name": "cross_validation",
            "description": "Perform k-fold cross validation on a dataset"
        },
        {
            "name": "analyze_structure",
            "description": "Analyze the structure of atomic data and convert to graph representation info"
        },
        {
            "name": "compare_models",
            "description": "Compare performance of different GNN models on a dataset"
        },
        {
            "name": "get_dataset_info",
            "description": "Get information about a dataset directory"
        }
    ],
    "supported_models": [
        "CGCNN_demo",
        "MPNN_demo", 
        "SchNet_demo",
        "MEGNet_demo",
        "GCN_demo",
        "SOAP_demo",
        "SM_demo"
    ],
    "dependencies": [
        "torch",
        "torch-geometric",
        "ase",
        "pymatgen",
        "fastmcp",
        "numpy",
        "scipy",
        "scikit-learn"
    ],
    "python_version": ">=3.8",
    "created_at": "2025-12-03",
    "transport_modes": ["stdio", "http"]
}