"scikit-learn" matching MCP servers:

Provides a standardized interface for interacting with Scikit-learn models and datasets, enabling training, evaluation, and model management through natural language.

An MCP server for agent-guided DSA practice that generates LeetCode-style problems and provides tutoring with escalating hints, concept explanations, and progress tracking.

Enables building MCP servers using TypeSpec, with tools to scaffold, compile, and learn TypeSpec.

A monorepo for learning the Model Context Protocol with TypeScript, featuring example MCP servers for weather data and tax calculation.

Helps AI agents decompose goals into costed execution plans, checkpoint progress with deviation tracking, and learn from past project data.

基于FastMCP和scikit-learn构建的机器学习MCP服务器，提供30个工具覆盖分类、回归、聚类、降维、模型评估和数据预处理。

An MCP server that enables AI agents to automatically discover, install, and learn to use new tools without manual configuration.

Enables users to learn what MCP is and generate new MCP servers from an idea, including tool design and configuration files.

Provides tools for training, managing, and making predictions with scikit-learn's HistGradientBoostingClassifier models. It allows users to handle model lifecycles, including creation, evaluation, and serialization, via the Model Context Protocol.

Enables AI coding agents to learn, from locally read lockfiles, what changes between the dependency version installed and a target version, which of the user's files that upgrade touches, and which vulnerabilities it resolves. It also performs OSV-matched vulnerability scans, ranks upgrade plans, and stores decision memory, running entirely on the user's machine with no account or API keys.

A package manager for AI agents that connects LLMs to a global registry of capabilities, allowing them to autonomously discover, install, and learn new skills from a centralized repository.

A Model Context Protocol server that gives AI coding agents 16 tools to secure what they build: install the one-line Nemesis Shield SDK, learn/approve/enforce app protection, put a domain behind a Cloudflare-like edge, guard LLMs (OWASP LLM Top 10), and create + score Omniguard business-logic fraud rules. Runs locally over stdio; the discovery tools work with no account.

Shared memory layer for AI coding agents to store and recall learned information across sessions and tools.

Enables AI agents to learn from their sessions by inspecting history, proposing rules or skills, and automatically persisting them to configuration files.

Enables LLM agents to interact with the Lean theorem prover through the Language Server Protocol, providing tools for analyzing Lean projects, accessing diagnostics, goal states, documentation, and searching for theorems using both local and external search services.

Enables AI coding agents to perform Lean 4 formal verification, navigate project symbols offline, and inspect C FFI bindings.

Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.

Enables AI-powered journaling in Obsidian with dynamic reflection prompts generated from recent entries and automatic backlinks between related diary entries. Supports adaptive templates that learn from writing patterns and smart content similarity linking.

A self-hosted MCP server for Linear that reduces response sizes by field-selecting minimal data, saving tokens and costs in LLM agent sessions.

Provides access to a community knowledge base of 16k+ debugging solutions and 223+ reusable skills, plus auto-scanned project-specific knowledge bases that learn and store solutions as you work.