YouTube Transcript AI MCP server

Find where a video talks about something. Give one or more keywords and get back only the matching passages, each with a timestamp and a link that opens the video at that moment. Far cheaper than reading the whole transcript when you are looking for specific points. Matching is a case-insensitive text match, so try synonyms and spelling variants (auto-captions often mis-hear names).

Show the plan of the API key on this connection (Free or Pro), the daily request quota, how many requests were used today, how many remain, when the quota resets, and the per-call limits of the tools. Free: it does not use any quota. Call it before a large job to check the quota is enough.

Read what was said around a moment in a video: the sentences before and after a timestamp. Use it after search_spoken_words to see what a matching sentence was part of, before you quote or rely on it. Works for videos in the library only.

Read the transcripts of several videos in one call (up to 5), each cut to a length you choose, so you can skim a set of search results without filling your context. Each video counts as one request against your daily quota. For the rest of a long video use find_in_transcript or get_youtube_transcript.

Fetch the top comments of a YouTube video (YouTube's "top" ordering, replies not included), with like counts. Comments are where viewers report what worked for them, which makes them useful for checking claims made in the video.

Fetch the full text transcript of a YouTube video. Accepts a YouTube URL (watch, youtu.be, shorts, embed) or a bare 11-character video ID, and returns the transcript as plain text with title and metadata. Optionally pass a BCP-47 language code to select a specific caption track. Very long transcripts (multi-hour videos) come back in parts of up to 120,000 characters: when there is more, the result ends with a note giving the offset to pass on the next call.

List the latest videos of a YouTube channel or playlist, with view count and duration. Use it to follow a creator who looks credible, or to cover a playlist. Returns at most 50 videos per call; to go further, call again with offset set to the number of videos already read (the result ends with the offset to use). Reaches up to the first 500 videos.

Find which channels talk about something the most, ranked by mentions, with how many of their videos mention it. Use it to find who covers a topic: creators to follow, experts, or partners. Same library, caption quirks and dates as search_spoken_words.

Find the videos that talk about something the most, ranked by how many sentences mention it. Better than searching titles when you want the videos that really go deep on a topic. Each result has the number of mentions, the first mention and a link. Read the best ones with get_youtube_transcript or find_in_transcript. Same library, caption quirks and dates as search_spoken_words.

Search what people actually say inside YouTube videos. Full-text search over about 100 million spoken sentences from about 425,000 English-language videos (the ones users looked up on youtube-transcript.ai), with typically 1 to 8 million new sentences added every day. Returns matching sentences, each with the video, channel and a link that opens the video at that moment. Use it to find evidence and quotes, to see how people talk about a topic, product or phrase, or to find which videos mention something. Captions are auto-generated and often mis-hear names (for example "Claude Code" is often written "cloud code"), so pass likely mis-hearings in also_match. The library is a sample of YouTube, not all of it. Dates are the day we captured the captions, not the day the video was published.

Search YouTube and return matching videos with channel, view count and duration. Use it first when researching a topic: run several differently-worded queries (and in more than one language), then read the best hits with get_youtube_transcript, find_in_transcript or get_transcripts_batch. Optional filters on views and length.

Count how often something is mentioned over time, by day, week or month. Each period shows the mentions and the rate per million spoken sentences, so busy and quiet periods compare fairly. Use it to see whether a topic is rising or fading. The date is when we captured the captions, not when the video was published, and the first day (2026-09-13) holds a one-off backfill of earlier captions, so compare the rate, not the raw count, and be careful with that first period.