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.
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.
63 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
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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.
17
MIT
Why this server?
Enables use of scikit-learn library for machine learning within executed Python code.
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.
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Why this server?
Provides tools for analyzing scikit-learn ML pipelines, model training, and feature engineering.
A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.
1
MIT
Why this server?
Generates an MCP server for scikit-learn, exposing its importable top-level functions, such as get_config, as tools.
Enables automatically generating standalone MCP servers from any Python library, exposing its functions as deterministic callable tools.
MIT
Why this server?
Allows loading and analyzing scikit-learn models (.pkl/.joblib), with tools for evaluation, feature importance, threshold analysis, and report generation.
Enables natural-language evaluation, explanation, and reporting of any trained ML model, including performance metrics, prediction insights, drift detection, and PDF report generation.
