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WhisperAI

Translate transcript

translate_transcript
Idempotent

Starts translating a completed transcript into another language. Translation runs asynchronously and uses the user's plan quota. Call it when the user asks for a transcript in a different language; if a translation already exists it is returned instead of being redone.

Input Schema

NameRequiredDescriptionDefault
idYesRecording id from list_transcripts
targetLanguageYesTarget language code, e.g. "es", "fr", "de", "ja"

Output Schema

NameRequiredDescriptionDefault
statusYes"processing" while the translation job runs, "completed" when done
contentNoThe translated text when the translation is already completed
messageYesWhat happened / what to do next
recordingIdYesThe recording id
targetLanguageYesThe requested target language

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the annotations, the description discloses three things the agent cannot infer: the work runs asynchronously, it consumes the user's plan quota, and an existing translation is returned rather than regenerated. That is real added value on top of readOnlyHint=false and idempotentHint=true. It stops short of telling the agent how to observe completion or retrieve the result of an async job.

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?

Two sentences, front-loaded with the action, then the trigger condition and the dedup caveat. Nothing is repeated from the title and every clause carries distinct information.

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

Completeness4/5

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

With an output schema present, return values need no explanation, and the description covers the async, quota, and caching behaviors that matter for invoking it. The one residual gap is how an agent should follow up on a job started asynchronously — whether by polling get_transcript or some other means.

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 coverage is 100%, so both parameters (id, targetLanguage) are already documented with sourcing and example language codes. The description adds only the notion of a "completed" transcript as a validity condition and does not extend parameter meaning further, which is the expected baseline when the schema does the work.

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 (translating) on a specific resource (a completed transcript) into a new language. The verb is unique among siblings — retranscribe_transcript, get_transcript, export_transcript all describe clearly different operations — so an agent can route 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?

"Call it when the user asks for a transcript in a different language" gives an explicit trigger condition, and "if a translation already exists it is returned instead of being redone" tells the agent re-invocation is safe. What is missing is a stated exclusion — e.g. that it cannot be used on in-progress recordings, or that retranscribe_transcript is the alternative when the source language itself is wrong.

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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