"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.

MCP server that lets AI assistants access University of Waterloo LEARN course data (announcements, content, grades, assignments, outlines) and search past assessments in Google Drive.

An MCP server that gives desktop AI apps access to your Waterloo LEARN courses, enabling listing courses, getting announcements, content, grades, and upcoming events 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.

Enables AI agents to design RF filters and SMPS-EMC from spec using three MCP servers that drive LTspice, Qucs-S, and scikit-rf, with closed-form synthesis, real-component optimization, and CISPR-aware compliance checking.

Enables AI assistants to browse your own university courses by downloading files, slides, announcements, deadlines, grades and lecture transcripts to your machine, then answering questions and searching across them through MCP tools. It logs in with your own account and refreshes only what changed.

Enables users to record Windows desktop and browser workflows once and generate replayable Cursor Skills, using Playwright MCP and Windows Computer MCP for semantic, non-coordinate replay.

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

An MCP server that makes your AI agent learn and evolve by capturing feedback as reusable, persistent skills that improve over time.

A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.

Distills Claude Code's 310K-line source into 32 queryable modules via MCP, enabling AI agents to learn and reuse Claude Code's architectural patterns through natural language.

Lets an AI agent read and change on-premises Active Directory by writing short JavaScript scripts that run in a network-isolated sandbox against a chosen domain. Runs on Windows as the logged-on user, over Kerberos, with tools for executing scripts, searching the domain's schema catalogue, querying Microsoft Learn docs, and managing domain connections and Group Policy objects.

Enables AI coding agents to evaluate code snippets and learn from execution outcomes through local bio-inspired associative memory, delivering instant zero-token bug and antipattern reflexes without LLM inference.

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.

MCP server for persistent, compounding memory that automatically captures corrections and insights across AI sessions, enabling agents to learn and improve over time.

Enables Claude to remember conversations and learn over time by storing and recalling messages, memory abstracts, and recent history using a local SQLite database.

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.

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

Enables low-cost agent models to control Windows applications through a compact, state-safe proxy over Open Computer Use, reducing model-visible context by up to 99.8% with support for record/replay and reusable UI component memory.