"YouTube Shorts" matching MCP servers:

Turn any YouTube video, article, PDF, or image into a reusable Claude Code skill — without leaving your editor.

Public MCP wrapper for SubDownload.Fetch YouTube transcripts, search videos, browse channels and playlists — instant YouTube data for your AI workflow.

Enables agents to check and retrieve concept-structured YouTube video summaries from the oratilo library via lookup and full-text search tools, avoiding redundant summarization work.

Persistent knowledge-base MCP server. Semantic search with source citations over saved YouTube videos, podcasts, articles and PDFs — one endpoint for every MCP-capable AI client.

MCP server that ingests YouTube videos and podcasts, making them searchable with timestamps and deep links to exact moments.

Local MCP server that turns Mark Builds Brands' YouTube knowledge into an AI coaching assistant for ecommerce and Facebook Ads.

Enables AI assistants to fetch YouTube transcripts with timestamps, search within a video, and keep a local searchable memory of every transcript fetched.

Transforms YouTube into a queryable knowledge source with search, video details, transcript analysis, and AI-powered tools for summaries, learning paths, and knowledge graphs. Features quota-aware API access with caching and optional OpenAI/Anthropic integration for advanced content analysis.

Transforms YouTube videos into a persistent, structured knowledge base using transcripts and visual frame analysis, enabling knowledge compounding and natural language querying.

MCP server for turning YouTube channels into a queryable local knowledge base. Enables BM25 search and extractive Q&A over captions with timestamped citations.

Enables Claude to automatically extract entities and relationships from URLs, PDFs, and YouTube videos to build structured knowledge graphs in Neo4j. It supports custom schemas, academic citation extraction, and community detection for advanced research and content analysis.

Converts YouTube and Bilibili videos into structured knowledge articles using local transcription or subtitle extraction combined with AI-powered summarization. It supports multiple summary styles and provides tools to process URLs, track job status, and retrieve results directly within MCP-compatible agents.

Enables saving URLs, local files, plain text, and platform content (WeChat, X, YouTube, PDF, images, audio/video, Telegram) as structured Markdown knowledge cards in an Obsidian vault, with optional AI-powered classification and tagging.

An MCP knowledge server that enables saving and retrieving memory across sessions, with tools to ingest text, URLs, YouTube, and files, and perform semantic search.

Connects your Noverload saved content library to AI assistants, enabling search, access, and management of saved YouTube videos, articles, PDFs, and other content. Features advanced search modes, action item tracking, and goals management through natural language.

An MCP server that gives AI assistants a local YouTube memory: transcribe videos, store and search transcripts, follow subscriptions, and generate summaries, timestamped highlights, and digests, all on your machine with no cloud or API keys.

Enables users to ingest YouTube videos, analyze transcripts and scene-change frames, and query a compounding wiki knowledge base through MCP clients like Claude and Copilot.

Enables users to turn YouTube videos into a compounding, searchable knowledge base by combining transcripts with vision analysis and agentic retrieval, while serving that capability to MCP clients like Claude and Copilot.

Enables AI agents to semantically search, retrieve, and augment an org-roam knowledge graph via MCP, with features like vector-based search, YouTube ingestion, and local LLM processing.

Enables transforming YouTube videos into a persistent, structured knowledge base by extracting transcripts and scene-change frames, then reasoning over compiled wiki pages for agentic search through MCP clients.

Enables users to ingest YouTube videos and query a persistent, compounding knowledge wiki built from transcripts and scene-change visual analysis, with agentic search and source citations through MCP clients.

Enables MCP clients to turn YouTube videos into a persistent, searchable knowledge base using transcripts and vision analysis, then query that compiled wiki through agentic retrieval.

Enables ingesting YouTube videos into a persistent, searchable wiki with transcript extraction, scene-change vision analysis, and cross-video knowledge compounding. MCP clients can then ask questions and receive reasoned answers with source citations.

Enables users to ingest YouTube videos, extract transcripts and scene-based visual descriptions, build a compounding wiki knowledge base, and query it through agentic search from MCP-compatible clients.

Enables users to ingest YouTube videos and have transcripts plus visual scene analyses compiled into a growing, cross-linked wiki that can be queried agentically for answers with citations.

Enables users to ingest YouTube videos into a persistent, cross-referenced knowledge base by extracting transcripts, analyzing scene-change frames with vision models, and providing agentic search and cited answers through MCP clients like Claude and Copilot.

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Enables MCP clients to ingest YouTube videos into a compounding wiki by combining transcripts, scene-change vision analysis, and persistent cross-referenced knowledge pages. It lets agents search and reason over that compiled knowledge with source citations.

Enables ingesting YouTube videos to build a persistent, cross-referenced wiki from transcripts and vision-analyzed frames, then supports agentic search and question answering through MCP clients like Claude and Copilot.

Enables users to ingest YouTube videos and query a persistent, compounding wiki built from transcripts and visual scene analysis, with agentic search over merged entities, topics, and concepts. It exposes this knowledge base as an MCP server so clients like Claude and Copilot can search and reason over the wiki.

Enables users to transform YouTube videos into a persistent, compounding knowledge base by extracting transcripts and scene-change visual descriptions into cross-referenced wiki pages that MCP clients can query with agentic search.

Builds a searchable knowledge base from YouTube video transcripts with hybrid semantic and keyword search. Allows LLM assistants to search, organize, and retrieve timestamped information from videos you've watched.

Local-first MCP server that captures web URLs (X, Reddit, YouTube, Wikipedia, articles) as typed data + Markdown into a self-hosted capture/store/recall substrate, with offline semantic recall. Six tools over a local khiipd daemon; run khiipd serve first.

Five tiny, single-purpose MCP servers for AI agents — arXiv search, YouTube transcripts, web-to-Markdown, market quotes & FX, and a persistent memory vault. No API keys, no bloat.

Enables AI agents to build, search, compare, and keep up to date an Obsidian knowledge vault from YouTube creators, with timestamped and code-verified claims, shared topic notes, and saved outputs.

Enables AI agents to safely interact with golf course operations by providing real-time inventory, booking rules, pricing policies, and a self-learning memory system.

Quizlar is the learning MCP server. It turns whatever source the user brings — a YouTube lecture, a PDF, a URL, a textbook passage, or a pasted block of notes — into flashcards, then runs interactive quizzes with FSRS spaced-repetition scheduling so the material actually sticks. Every tool maps one-to-one to something a real learner does: ingest, quiz, track progress, review what's due.

Enables AI agents to search, read, and safely modify markdown notes in a server-hosted vault over HTTP, with OAuth or machine-token access, atomic writes, conflict checks, dry-run diffs, trash-based deletion, and an audit journal. Also turns YouTube links into transcripts and notes stored in the same vault.

Enables ingesting YouTube videos into a persistent wiki knowledge base by combining transcripts with scene-change frame analysis, then answering questions over the compiled pages via full-text search and agent reasoning. Serves raw candidate pages and a wiki table of contents to MCP clients so their own model can reason without requiring an API key.

Enables AI agents to capture, classify, promote, synthesize, and retrieve persistent knowledge from sources such as Obsidian vaults, code repos, YouTube transcripts, bookmarks, terminal sessions, and bug fixes. Exposes tiered cold/warm/hot memory with token-optimized search, context briefings, and cross-source synthesis so multiple agents share one compounding brain across sessions.