"scikit-learn" matching MCP servers:

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.

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.

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

An MCP server serving as a structured knowledge base of crypto whitepapers for AI agents to access, analyze, and learn from.

Enables persistent project memory for Hermes Agent by storing and retrieving wiki, code, and learning layers locally, with tools to recall, learn, retire, and ingest information beyond the context window.

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.

An MCP server implementation that helps Claude remember and learn from programming errors by storing, retrieving, and finding similar errors with full context.

Local workspace memory for Claude Desktop. Indexes documents into a vector store with hybrid search, cross-session memory, auto-learn, and knowledge graph.

Enables file-based knowledge management with ranked keyword and semantic hybrid search, allowing AI agents to learn from documents and recall relevant knowledge as a persistent memory tool.

Intercepts Claude conversations via MITM proxy and stores them in Mem0, enabling Claude to search and recall past conversations, learn user preferences, and maintain context across sessions.

A code ingestion tool that transforms your code into AI-optimized prompts instantly. Gather the relevant context with code2prompt under the hood. Learn more at code2prompt.dev

An MCP server for ingesting Lean Six Sigma PDFs into a searchable multimodal knowledge base.

Enables AI clients to search a personal corpus, read relevant source passages, and answer questions with traceable evidence from imported sessions, notes, and articles.

MCP server that converts URLs into token-minimized clean text for LLMs, providing a receipt of token and cost savings.

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.

mozg turns documentation into knowledge brains your agent queries over MCP instead of loading whole files into context. What makes it different: every brain sits an exam. Its goal becomes ~30 control questions, re-sat after every ingest — so 'trained 92%' is measured, not claimed, and the failing questions are listed publicly. Brains learn from use: searches that return nothing become exam questio

Enables any MCP-capable agent to write and maintain its own wiki of small linked markdown pages with a typed ontology, storing, indexing and retrieving them through keyword (BM25) search plus ontology-graph traversal, typed-relation management, page reads and writes, linting and listing. It also offers an optional vision tool that extracts objects and text from images so text-only models can learn from screenshots, charts and scans.