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Transcribe video or audio

create_transcription

Start a timed speech-to-text job for a public video or audio link to get text, SRT subtitles, or timestamps without making clips. Use no credits; call get_transcription for results.

Instructions

Starts a timed speech-to-text transcription of a video or audio link. Free: spends no credits. Supports the same links as create_clip_job (YouTube, VK Video, RuTube, Dzen, Twitch, Yandex Disk, Google Drive and direct media links); local files are not supported.

Use it when the user needs the text, subtitles (SRT) or timestamps of a video without making clips. For a video that already has a completed clip job, create_article returns the full timed transcript together with an article.

language is a two-letter code such as en or ru; omit it to auto-detect (one language for the whole recording).

Returns transcription_id right away. It usually finishes in 1 to 5 minutes; long recordings take longer. Next, call get_transcription with wait_sec=50.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoTwo-letter language code of the speech, e.g. en or ru. Omit to auto-detect. Never pass 'auto'.
source_urlYesPublic link to the video or audio.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
next_stepYes
transcription_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.9/5.0
Behavior5/5

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

Adds substantial behavior beyond the annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=false): it discloses cost ('Free: spends no credits'), the accepted source platforms, the async contract ('Returns transcription_id right away'), expected latency (1-5 minutes), and the follow-up call with wait_sec=50. This is far richer than the structured hints alone.

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

Conciseness5/5

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

Front-loaded with purpose and cost, then links, then usage routing, then the parameter note, then the async return contract. Every sentence carries distinct information (cost, platforms, exclusion, alternative, polling step) with no filler.

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 two-parameter async creation tool with a full output schema and annotations, the description supplies everything an agent needs: what it does, when to prefer alternatives, input constraints, cost, latency, and the exact next call. Nothing material is missing.

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

Parameters4/5

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

Schema coverage is 100% and the description largely echoes the schema's own language guidance (two-letter code, omit to auto-detect), so it earns the baseline-3-and-up territory. It adds real value with the constraint that auto-detect yields 'one language for the whole recording', which the schema does not state.

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?

States a specific verb+resource ('starts a timed speech-to-text transcription of a video or audio link') and clearly distinguishes itself from siblings: it names create_clip_job for supported links and create_article as the alternative when a clip job already exists. An agent can tell this apart from create_clip_job and create_article without opening any schema.

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?

Explicit when-to-use ('when the user needs the text, subtitles (SRT) or timestamps of a video without making clips') plus a named alternative with its selecting condition (create_article for videos with a completed clip job), and an explicit exclusion (local files not supported). Nothing is left to inference.

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