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
Use your own trained text and picture classification models from any AI assistant.
List datasets, schemas, run APL queries, and use prompts for exploration, anomalies, and monitoring.
Machine-readable utilities and datasets for AI agents.
Governed AI agent skills — one library, distributed to devs and exposed to remote agents over MCP.
