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.
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.
Last updated 6 months ago
MIT
Why this server?
Support for installing and using scikit-learn in Python containers as demonstrated in examples
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.
Last updated a year ago
4
MIT
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.
A Model Context Protocol server that enables AI assistants to fetch and understand GitHub repository documentation on-demand from DeepWiki during conversations.
Last updated 9 months ago
3
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.
Last updated a month ago
4
MIT
Why this server?
Implements various machine learning algorithms from scikit-learn including regression and classification models as part of the ML training platform
Advanced machine learning platform with MCP integration that enables automated ML workflows from data analysis to model deployment, featuring smart preprocessing, 15+ ML algorithms, and interactive visualizations.
Last updated 10 months ago
Why this server?
Mentioned as an example package that can be installed and used in the isolated containers for machine learning tasks.
Provides isolated Docker environments for code execution, enabling users to create and manage containers, execute multi-language code, save and reproduce development environments, ensuring security and isolation.
Last updated a year ago
17
Why this server?
Incorporates scikit-learn for machine learning utilities, including the TF-IDF model implementation for traditional text analysis.
A Python-based Model Context Protocol server that integrates local AI models for managing data with features like CRUD operations, similarity search, and text analysis.
Last updated 9 months ago
Why this server?
Mentioned as an optional enhancement for implementing machine learning models to replace random predictions with historical yield analysis and portfolio optimization
Enables interaction with Pendle Finance DeFi protocol to fetch live yields, simulate staking and swaps, retrieve portfolio data, and get AI-based token recommendations. Provides comprehensive DeFi portfolio management and yield optimization through natural language.