"How to find or generate YouTube video transcripts" matching MCP servers:

Enables AI assistants to analyze and summarize YouTube videos by extracting captions, subtitles, and comprehensive metadata including title, description, and duration in multiple languages.

Extracts captions, metadata, and descriptions from YouTube videos to enable AI assistants to summarize their content.

Extracts transcripts or spoken captions from multi-platform video links (Bilibili, YouTube, Douyin) and returns concise Markdown summaries or full transcripts, saving tokens by keeping full text out of the conversation.

A Model Context Protocol server that analyzes YouTube videos, enabling users to extract transcripts, generate summaries, and query video content using Gemini AI.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Allows AI agents to fetch, search, and summarize YouTube video transcripts without an API key, including chapter summaries.

MCP server that fetches YouTube video transcripts and optionally summarizes them. Supports multiple transcript formats (text, JSON, SRT, WebVTT), multi-language retrieval, and flexible YouTube URL parsing.

An MCP server for intelligent YouTube video analysis that provides token-optimized summaries, sentiment analysis, and entity extraction from transcripts. It enables AI assistants to perform video reporting, channel monitoring, and comprehensive YouTube searches through structured data tools.

A Model Context Protocol server that enables access to YouTube video content through transcripts, translations, summaries, and subtitle generation in various languages.

Bridges YouTube API and AI assistants, enabling video analysis by downloading and processing closed captions to create summaries of YouTube videos.

Provides MCP tools for fetching timestamped YouTube transcripts and top comments, scanning spreadsheets for video links, and a local web UI for grounded video summaries.

Enables automated video learning workflows by ingesting video URLs, managing remote GPU ASR transcription, pulling transcripts, generating digest summaries, and searching local notes via MCP tools.

Enables users to submit Douyin/Bilibili/YouTube links or local audio/video and create reusable processing jobs, then retrieve timestamped transcripts, SRT subtitles, summaries, sampled screenshots, exports, and optional AI-rewritten scripts through MCP tools sharing a local FastAPI job store.

Enables turning public YouTube videos into full text and a short summary by having Gemini watch the video through its documented YouTube-URL input, with no downloading or caption scraping. Digests are cached in SQLite by video id, so repeated requests are free, and stored videos can be listed or searched with full-text queries.

Enables AI language models to interact with YouTube content through a standardized interface, providing tools for retrieving video information, transcripts, channel analytics, and trend analysis.

MCP server that converts PDF, video, web, and audio inputs into structured Markdown notes with support for checkpointing, batch processing, and Obsidian integration.

Enables LLMs to interact with YouTube videos by fetching transcripts, summarizing content, and answering questions based on video context.

A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.

Enables AI agents to search, watch, summarize, clip, and extract transcripts from YouTube videos, all without needing an API key or leaving the chat.

Enables fetching, searching, and summarizing YouTube video transcripts with multi-language support.