Allows interaction with Scikit-learn, providing tools for training and evaluating models, data preprocessing, feature engineering, model persistence, and hyperparameter tuning.
Click on "Deploy 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., "@mcp-server-scikit-learn classify iris species using a support vector machine"
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.
This is a Model Context Protocol server for Scikit-learn, providing a standardized interface for interacting with Scikit-learn models and datasets.
Related MCP server: HistGradientBoostingClassifier MCP Server
Train and evaluate Scikit-learn models
Handle datasets and data preprocessing
Model persistence and loading
Feature engineering and selection
Model evaluation metrics
Cross-validation and hyperparameter tuning
This project is not yet set up for ephemeral environments (e.g. uvx usage). Run this project locally by cloning this repo:
git clone https://github.com/yourusername/mcp-server-scikit-learn.git
cd mcp-server-scikit-learnYou can launch the MCP inspector via npm:
npx @modelcontextprotocol/inspector uv --directory=src/mcp_server_scikit_learn run mcp-server-scikit-learnUpon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
OR Add this tool as a MCP server:
{
"scikit-learn": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-server-scikit-learn",
"run",
"mcp-server-scikit-learn"
]
}
}Create and activate a virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activateInstall dependencies:
pip install -e ".[dev]"Run tests:
pytest -s -v tests/This server cannot be deployed
List datasets, schemas, run APL queries, and use prompts for exploration, anomalies, and monitoring.
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