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Submit transcription job

revai_submit_job
Destructive

WRITE: start an asynchronous machine transcription job for an audio or video file at a public URL. It uses Rev.ai credit (free credit first). Returns the job with status in_progress; then poll revai_get_job and read the result with revai_get_transcript. Human transcription is not offered. POST /jobs.

Input Schema

NameRequiredDescriptionDefault
languageNoSpoken language as an ISO 639-1 code (en, en-us, en-gb, es, fr, de, ...). Default en.
metadataNoA note stored with the job and returned with it (for example your own reference).
verbatimNoKeep every false start and disfluency (default true for machine jobs).
media_urlYesDirect download URL of the audio or video file. Rev.ai fetches it; it must be reachable without login.
speakers_countNoNumber of speakers, if known (English, Spanish, French); improves speaker labels.
filter_profanityNoMask profanity in the transcript.
forced_alignmentNoMore accurate per-word timestamps (en, fr, it, de, es).
skip_diarizationNoDo not label speakers.
skip_punctuationNoReturn words without punctuation.
summarization_typeNoAlso create a summary, read later with revai_get_summary.
remove_disfluenciesNoDrop 'ums' and similar fillers.
delete_after_secondsNoDelete the job and its data this many seconds after it completes.
speaker_channels_countNoNumber of audio channels with one speaker each. Billed per channel.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=true, idempotentHint=false, and openWorldHint=true. The description adds valuable context beyond these: it is asynchronous, returns a job with status in_progress, consumes Rev.ai credit (free credit first), and requires the media to be at a public URL. It does not detail auth needs or rate limits, but the added workflow and cost context is substantial.

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-loads 'WRITE:' and the core action, then gives the follow-up workflow in two compact sentences. Every sentence carries useful information and there is 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 13-parameter write tool with no output schema, the description supplies the essential behavioral context: async job creation, credit consumption, returned status, and exact follow-up tools for polling and retrieving the transcript. The schema fully covers parameter details, so nothing critical is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all 13 parameters thoroughly. The description adds only a slight framing ('audio or video file at a public URL') that largely restates the media_url schema description, so baseline 3 is appropriate.

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 and resource ('start an asynchronous machine transcription job') and clearly distinguishes it from human transcription, which is not offered. It also names the sibling tools for follow-up (revai_get_job, revai_get_transcript), so an agent can identify its place in the workflow 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 Guidelines4/5

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

Provides clear context for when to use it (to start a machine transcription job for a public media URL) and explicitly routes to revai_get_job and revai_get_transcript afterward. It also notes that human transcription is not offered, but does not compare against other submission alternatives (none exist) or detail failure conditions.

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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