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Translate audio

translate_audio
Read-only

Converts spoken audio in supported languages into English text using an OpenAI speech-to-text model, with optional prompts and output formats for transcription workflows.

Instructions

Translate spoken audio in any supported language into English text using an OpenAI speech-to-text model. Output may occasionally come back untranslated, in its original language

Input Schema

NameRequiredDescriptionDefault
fileYesAudio to translate, as an http(s) URL or a file path on this server's filesystem (flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm)
modelYesOpenAI model to use for audio translation
formatNoOutput format. 'verbose_json' includes segment timestampsjson
promptNoOptional English text to guide the model's style or vocabulary, or to continue a previous audio segment

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, so the safety profile is already covered. The description adds a genuinely useful non-obvious trait beyond the annotations: output may occasionally return untranslated in the original language, which an agent would otherwise not anticipate and would need to validate.

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?

Two sentences, both earning their place: the first declares the operation, the second flags the reliability caveat. The core purpose is front-loaded with no filler or restatement of the title.

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 no output schema, the description carries return-value burden, and it does partly cover this by noting the possible untranslated output; the format parameter further documents output shapes. Missing only minor context like the language of the prompt or model selection guidance.

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 file, model, format, and prompt are already fully documented, establishing the baseline of 3. The description contributes no additional parameter meaning such as model choice or prompt behavior, so it neither compensates nor detracts.

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

Purpose4/5

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

States a specific verb (translate) plus resource (spoken audio) and the resulting artifact (English text), which inherently separates it from transcribe_audio's language-preserving behavior. It stops short of naming that sibling explicitly, so the differentiation must be inferred from the target language.

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

Usage Guidelines3/5

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

The 'into English text' phrasing implies the use case (foreign-language audio needing an English rendering), but there is no explicit statement of when to pick this over transcribe_audio, nor any prerequisites. Guidance is implied rather than stated.

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