mcp-server-scikit-learn: MCP server for Scikit-learn

What is mcp-server-scikit-learn?

The mcp-server-scikit-learn is a Model Context Protocol server that provides a standardized interface for interacting with Scikit-learn models and datasets. It is designed for developers who want to train, evaluate, and manage Scikit-learn models via the MCP protocol.

How to use mcp-server-scikit-learn?

Clone the repository locally, then launch the MCP inspector using npx @modelcontextprotocol/inspector uv --directory=src/mcp_server_scikit_learn run mcp-server-scikit-learn. Alternatively, add it as a MCP server in your configuration with the uv command pointing to the local directory.

Key features of mcp-server-scikit-learn

• 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

Use cases of mcp-server-scikit-learn

• Train and evaluate machine learning models through an MCP-compliant interface
• Automate data preprocessing and feature engineering pipelines
• Manage model lifecycle with persistent storage and loading
• Perform cross-validation and hyperparameter tuning remotely

FAQ from mcp-server-scikit-learn

Is this server ready for ephemeral environments like uvx?

No, this project is not yet set up for ephemeral environments; you must clone the repo and run it locally.

What are the runtime requirements?

The server requires Python and uses uv as the package runner; a virtual environment is recommended.

How do I debug the server?

Launch the MCP inspector via npm (npx @modelcontextprotocol/inspector ...) to get a browser-accessible debugging URL.

Can I use this server without cloning the repo?

Currently, a local clone is required; there is no uvx or pip-based one-line install described in the README.