Scribiz

Read the transcript of a video, one page at a time. Use it only when you need the words themselves (quote, translate, copy, review). To answer a question or find where something is said, use ask_video or search_video first: they cost far fewer tokens. Returns "[mm:ss] text" lines, at most max_chars characters (default 60000, about 15k tokens), and a nextCursor: call again with the same arguments p

Start here for any video. Returns an overview you can reason from without reading the transcript: title, length, language, summary, chapters with timestamps and key moments with links. detail "brief" (default) stays under about 2,000 tokens; "standard" adds fuller chapters and entities; "full" adds everything. include picks the parts to return (summary, chapters, key_moments, entities, transcript)

Ask a question about one video and get an answer with 3 to 5 cited moments (timestamps and links that open the video there). Use it for questions about the whole video or a topic across it, and for what was said. To find where a word or phrase is said, search_video is cheaper and exact. A question costs 0.1 minute on top of reading the video the first time. The answer is a model's reading of the v

Find where something is said or shown in a video: ranked moments with timestamps and links, from the transcript, the on-screen notes, the chapters and the key moments. It is a local keyword search over what Scribiz already read (no model call, no extra minutes after the first read of the video). Use it before get_transcript on any video longer than about ten minutes. query: words or a phrase. limi

Check a video run that returned status "processing". Pass its job_id exactly as given (it can be long: copy all of it). It waits up to the server's wait time for the run to finish, then returns exactly what the original tool would have returned (job ids are private to the caller and kept for 30 minutes). Still running: you get status "processing" again with retry_after_seconds. An unknown or expir