Goldilocks MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Goldilocks MCP ServerEstimate k-point spacing for BaGa4.cif at 0.95 confidence with ALIGNN model."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Goldilocks MCP server
Provides k-point generation tools for Quantum ESPRESSO with SSSP1.3 PBEsol efficiency version of pseudo-potentials
Tools exposed:
estimate_kpoint_distance
Requires specification path to the structure file, confidence level (models are trained for levels 0.85,0.9, and 0.95), and the model (ALIGNN or RF)
Example prompt: "Can you please generate k-points spacing for structure 'path/to/BaGa4.cif', confidence level 0.95 with ALIGNN model?"
Outputs the predicted k-spacing, and the confidence interval
generate_kpoint_grid
Requires specification path to the structure file, confidence level (models are trained for levels 0.85,0.9, and 0.95), and the model (ALIGNN or RF)
Example prompt: "Can you please generate k-points grid for structure 'path/to/BaGa4.cif', confidence level 0.95 with ALIGNN model?"
Outputs the predicted kmesh, generated using the lower bound of k-spacing interval (to make sure that the probability that predicted value is in agreement with confidence level)
Related MCP server: abacus-mcp-server
Installing MCP-server locally
Install uv (https://docs.astral.sh/uv/getting-started/installation/)
Clone repository
git clone https://github.com/stfc/goldilocks-mcp.git
cd goldilocks-mcpCreate virtual environment and install dependencies
uv venv --python 3.11
source .venv/bin/activate
uv pip install -e .Install pytorch-geometric (can't be installed from pyproject.toml but is required). See details https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html
uv pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.8.0+cpu.html
uv pip install torch_geometricAdding mcp to Claude Desktop
To add goldilocks-mcp to Claude Desktop:
Open or create the Claude Desktop configuration file:
macOS/Linux:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows: See instructions at https://modelcontextprotocol.io/docs/develop/build-server
If the file doesn't exist, create it with the content from
claude_desktop_config.json. If it already exists, merge thegoldilocks-mcpentry into the existingmcpServersobject.Important: Update the path in the config file. Replace
"absolute/path/to/goldilocks-mcp/goldilocks_mcp/"with the actual absolute path to thegoldilocks_mcpdirectory in your cloned repository.
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