Scikit-learn is a free, open-source machine learning library for Python that provides simple and efficient tools for data analysis and modeling, including classification, regression, clustering, and dimensionality reduction algorithms.
Exposes scikit-learn machine learning workflows as MCP tools: training Logistic Regression and KNN classifiers on the Wisconsin breast cancer dataset, running predictions on patient feature inputs, computing confusion-matrix metrics (accuracy, precision, recall for the malignant class), selecting the best model by a configurable rule, and generating global and local SHAP explanations for the selected model.
Enables training, predicting, evaluating, and explaining Logistic Regression and KNN models for breast cancer classification through MCP tools. It also supports model selection, SHAP explanations, and Parquet audit exports.
8
GPL 3.0
Why this server?
Includes scikit-learn for local, offline machine learning workflows.
Serves an offline knowledge base — an aggregated corpus of reference works plus a RAG index over them and the user's own documents — to any local AI model over MCP. Enables offline assistants such as Open WebUI, OpenCode, or Jan to ground answers in searchable, provenance-tracked local content without any network connection.
107 npm
MIT
Why this server?
Supports fetching documentation for scikit-learn as part of its repository search capabilities, referenced in the example results.
A Model Context Protocol server that enables AI assistants to fetch and understand GitHub repository documentation on-demand from DeepWiki during conversations.
3
-
Why this server?
Powers ML-based layout optimization features including DBSCAN and K-means clustering for component grouping, K-NN position prediction for component placement, and pattern learning for Grasshopper definitions.
Facilitates isolated code execution within Docker containers, enabling secure multi-language script execution and integration with language models like Claude via the Model Context Protocol.
5
MIT
Why this server?
Offers scikit-learn documentation, machine learning examples, model training patterns, and data science best practices via Context7's API
Provides access to the Context7 API for searching up-to-date documentation, code examples, API references, and troubleshooting help across thousands of programming libraries and frameworks. Enables developers and AI agents to quickly find accurate documentation, compare libraries, get migration guides, and resolve coding issues.
-
Why this server?
Provides dedicated expert cards for machine learning engineering tasks, including Scikit-learn modeling, evaluation, and data validation.
SkillMesh is a retrieval router for MCP/LLM tool catalogs. It selects top-K relevant expert cards instead of loading every tool, reducing prompt size, by improving tool selection, and lowering token cost. Supports Claude MCP, Codex skills, and OpenAI-style function schemas.
7
MIT
Why this server?
Allows interaction with Scikit-learn, providing tools for training and evaluating models, data preprocessing, feature engineering, model persistence, and hyperparameter tuning.
Provides a standardized interface for interacting with Scikit-learn models and datasets, enabling training, evaluation, and model management through natural language.
18
MIT
Why this server?
Enables use of scikit-learn library for machine learning within executed Python code.
