ChemLint

MCP server that hands Claude 150+ molecular machine-learning tools — SMILES standardization, descriptor and fingerprint calculation, scaffold and similarity analysis, model training, and reporting — so cheminformatics ML workflows run through tool calls instead of hand-written Python.


Type	MCP server
Supplier	molML
Availability	GA — actively maintained
Pricing	Free / OSS (MIT)
Capabilities	Read/Write — computes locally on molecules you supply

How to install

Requires uv (Python 3.13+ is provisioned by uv); Cairo is optional for molecular-structure rendering.

Install and verify it starts (the pytest run is a one-shot check — Ctrl-C is not needed, it exits on its own; Claude Code/Desktop launch the server themselves via stdio):
```
git clone https://github.com/molML/ChemLint.git
cd ChemLint
uv sync
uv run pytest -m server -q
```
Claude Code — direct MCP add (replace /path/to/ChemLint with the absolute path of your clone — e.g., $(pwd) if you are still inside it from the previous step):
```
claude mcp add --transport stdio chemlint -- uv run --with "mcp[cli]" --directory /path/to/ChemLint mcp run ./src/chemlint/server.py
```

Claude Desktop — add to claude_desktop_config.json (replace /path/to/uv with the absolute path to your uv binary — which uv prints it — and /path/to/ChemLint with your clone’s absolute path):

{
  "mcpServers": {
    "chemlint": {
      "command": "/path/to/uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "--directory",
        "/path/to/ChemLint",
        "mcp",
        "run",
        "./src/chemlint/server.py"
      ],
      "enabled": true
    }
  }
}

(./install.sh from the clone automates the Claude Desktop config edit.)

What it does

150+ tools across 13 categories: data management, molecular cleaning (standardization, canonicalization, validation pipelines), descriptors and fingerprints (MW, LogP, TPSA, ECFP, MACCS, RDKit), scaffolds, similarity (Tanimoto), clustering, machine learning (33+ algorithms with cross-validation and hyperparameter tuning), statistics, visualization, quality reports, activity-cliff detection, outlier detection, and dimensionality reduction.

Primary use cases: SMILES dataset cleaning and QC, molecular descriptor/fingerprint featurization, QSAR/property model training, similarity and scaffold analysis.

Notes

stdio transport — Claude Code/Desktop launch the server process; you do not keep it running in a separate terminal. Molecular-structure image rendering needs Cairo installed; descriptor/ML tools work without it. A separately-developed sibling, derekvantilborg/molml_mcp, shares the “molecular machine learning MCP” description; the relationship is not documented upstream — Unverified — treat them as distinct projects until upstream clarifies.

Sources

molML/ChemLint

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