talkthrough-mcp

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process_url

Download one public video or audio URL, then run the full local pipeline: transcript, keyframes, OCR, timestamps, optional diarization. Repeat calls reuse the stored job without re-downloading.

Instructions

Download ONE public video/audio URL once (this is the only tool that uses the network), then run the same LOCAL pipeline as process_media: transcript, keyframes, OCR, wall-clock, optional diarization. Supported: direct https:// links to a media file (mp4/mov/webm/mkv/ogv/m4a/mp3/wav/ogg/flac), one public YouTube video (watch, youtu.be, shorts, a completed live), and any public video PAGE yt-dlp can read — Instagram (public reels/posts), TikTok, Wikimedia Commons, pages with an HTML5/HLS player, other sites as far as their yt-dlp extractor works anonymously (Vimeo does not). Not supported: playlists, channels, active live streams, private/members-only/age-restricted/DRM videos, cookies/logins; sites that hide a video behind a login or a bot wall fail with a clear reason. The downloaded source is kept inside the job, so extract_frame works later without network; the raw URL is never stored (only a hash, the provider id/host and a bounded title). A repeat call on the same URL serves the stored job without touching the network unless refresh=true. Job ids stay content hashes: the same video from two URLs is one job. YouTube and other pages need the optional [url] extra. The provider's upload date is NOT the recording start: wall_clock stays null unless recorded_at is passed. When NOT to use: for local files (process_media), or to re-fetch data you already processed (use the retrieval tools). Examples:

process_url(url="https://youtu.be/nHfGfEiVdE8") — one public YouTube video, defaults are right
process_url(url="https://www.youtube.com/watch?v=ID&list=PL...") — the playlist part is ignored: ONE video
process_url(url="https://cdn.example.com/recordings/standup.mp4") — direct https link to a media file
process_url(url="https://www.tiktok.com/@nasa/video/7…") — a public video page; origin.provider names the site
meeting from a link: process_url(url=..., diarize=true, num_speakers=3, vocabulary="Vera, Tom, OKR")
non-English narration: process_url(url=..., model="large-v3-turbo", language="ru")
known recording start: process_url(url=..., recorded_at="2026-09-05T14:00:00+02:00") — enables t_wall
the video changed on the provider → process_url(url=..., refresh=true): new download, maybe a new job_id
re-anchor or change the model on a stored URL job → process_url(url=..., recorded_at=..., force=true), no download
error mentions [url] → run uvx --python ">=3.11,<3.14" "talkthrough-mcp[diarization,url]" and restart
playlist / channel / live / private URL → clear error; pass a single public video URL instead
"bot check"/"sign-in" refusal on Instagram/TikTok → the site blocked anonymous access; report it, no workaround
origin.published_at is the provider's upload time, not when the recording was made — never use it as t_wall
after success continue with get_transcript / search / get_moment on the job_id — never re-download
anti-example: a file on disk → process_media(path=...); process_url is only for https URLs

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes
`force`	No
`model`	No
`diarize`	No
`refresh`	No
`language`	No
`vocabulary`	No
`recorded_at`	No
`num_speakers`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.4.0Sep 5, 2026

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover read-only/destructive hints, but the description adds substantial non-obvious behavior: it is the only network-using tool, downloaded sources are retained for later extract_frame, raw URLs are never stored, job ids are content hashes, repeated calls are cached unless refresh=true, and wall_clock stays null unless recorded_at is provided. No contradiction with annotations exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well structured into core definition, supported/unsupported inputs, behavioral caveats, when-not-to-use, and topical examples. Each section provides distinct operational information; the minor repetition about upload-date versus recording-start is acceptable given the risk of misuse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a network-capable tool with 9 parameters, no parameter descriptions, and an output schema present, the description is exceptionally complete. It covers invocation semantics, failure modes, caching/deduplication, privacy behavior, provider limitations, and follow-up workflow with retrieval tools. Nothing essential for correct use appears missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must carry the semantic burden. It does so via examples covering all nine parameters: url, refresh, force, model, language, diarize, num_speakers, vocabulary, and recorded_at. It also explains important parameter interactions, such as force avoiding re-download and playlist parts being ignored in url.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: download exactly one public video/audio URL and run the same local pipeline as process_media. It clearly distinguishes itself from local-file processing and retrieval tools, and enumerates supported and unsupported URL types, making the tool's scope unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes an explicit 'When NOT to use' section naming process_media and the retrieval tools, plus an anti-example for local files. The extensive examples also explain when to use refresh, force, diarize, model, language, vocabulary, and recorded_at, giving an agent concrete decision guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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