Thermophysical Data Curation MCP Server

An MCP server for auditable thermophysical data curation using five supplied JR-MPNN checkpoints and selected UManSysProp group-contribution methods.

What it provides

• inspect_molecule: validate and canonicalize a SMILES string and report domain flags.

• predict_jrmpnn: predict Tm, Tb, Tc, Pc, and Vc with Joback baselines.

• assess_jrmpnn_training_similarity: compare a query with the 10 nearest property-specific JR-MPNN training embeddings without returning private records.

• estimate_umansysprop: estimate boiling point, critical properties, density, and vapor pressure.

• curate_thermophysical_records: normalize, deduplicate, score, and triage records with an audit trail.

• server_capabilities: report available models, methods, and scientific limitations.

Predictions are diagnostic cross-checks. They do not replace accepted experimental measurements, and the server never silently deletes submitted records.

Install from source

Python 3.11 or newer is required. Installation may take several minutes because PyTorch, PyTorch Geometric, RDKit, and the scientific dependencies are substantial.

git clone https://github.com/frankmtetwa/thermophysical-curator.git
cd thermophysical-curator
python -m venv .venv

Activate the environment:

# Windows PowerShell
.\.venv\Scripts\Activate.ps1

# macOS/Linux
source .venv/bin/activate

Then install and test:

python -m pip install --upgrade pip
python -m pip install .
python -m unittest discover -s tests -v

Run the stdio MCP server with:

thermophysical-curator

The process waits for an MCP client on standard input. That behavior is expected.

Enable training-similarity assessment

The repository does not contain proprietary training compounds or generated embedding indices. On each computer where similarity assessment is required, create a private reference directory containing these five files:

private_reference/
|-- Tm_train_smiles.csv
|-- Tb_train_smiles.csv
|-- Tc_train_smiles.csv
|-- Pc_train_smiles.csv
`-- Vc_train_smiles.csv

Each CSV must contain a smiles column. A compound_id column is optional:

compound_id,smiles

Privacy-safe header-only examples are available in reference_templates/. Copy them to private_reference/, then populate them locally with the SMILES used to train the corresponding property model. Do not combine calibration or test compounds with the training set.

Build the private embedding indices after installing the project:

python -m curation_agent.similarity build

The default location is private_reference/ in the repository root. To keep the files elsewhere, set THERMOPHYSICAL_REFERENCE_DIR to an absolute directory before building the indices and before launching the MCP server. Restart the MCP client after building so it refreshes the available tool state.

The reported training-similarity percentile is an applicability-domain diagnostic, not a calibrated uncertainty interval or a guarantee of prediction accuracy.

Claude Desktop

MCPB extension

This repository contains an experimental cross-platform UV manifest. Install the MCPB CLI and build the extension from the repository root:

npm install -g @anthropic-ai/mcpb
mcpb validate manifest.json
mcpb pack . dist/thermophysical-curator-0.1.0.mcpb

In Claude Desktop, open Settings > Extensions > Advanced settings > Install Extension and select the generated .mcpb file. The UV runtime downloads the Python dependencies on first launch, so the initial startup can be slow. When Claude asks for the Private JR-MPNN reference directory, select the private_reference directory containing both the five CSV files and the generated embeddings/ directory. The private files remain outside the extension bundle.

Manual configuration

Users who prefer a pre-created virtual environment can add this to Claude Desktop's MCP configuration, replacing the command with the absolute path to their environment:

{
  "mcpServers": {
    "thermophysical-curator": {
      "command": "C:\\path\\to\\repo\\.venv\\Scripts\\python.exe",
      "args": ["-m", "curation_agent.server"]
    }
  }
}

On macOS/Linux, use /path/to/repo/.venv/bin/python instead.

Codex

After installing the project, register the server using the environment's Python:

codex mcp add thermophysical-curator -- /absolute/path/to/python -m curation_agent.server

Example curation record

{
  "smiles": "CCO",
  "property": "normal_boiling_point",
  "value": 78.37,
  "unit": "C",
  "source": "literature citation",
  "doi": "10.xxxx/example",
  "experimental": true,
  "method": "ebulliometry",
  "uncertainty": 0.1
}

Privacy

The package contains model weights but no training, calibration, or proprietary experimental records. Inputs are processed locally by the MCP server. The host AI application may still receive tool arguments and results, subject to that application's privacy policy.

Redistribution checklist

Before making the repository public, verify that you have permission to redistribute the five JR-MPNN .pth checkpoint files. They are required for model predictions but their redistribution terms were not present in the supplied source directory.

UManSysProp-derived files retain their original copyright notices and GPL terms. See THIRD_PARTY_NOTICES.md and LICENSE.

License

GPL-3.0-or-later, subject to the third-party notices and checkpoint redistribution rights described above.