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audio-transcribe

Transcribe audio from public URLs using Whisper. Supports mp3, mp4, wav, and more up to 24 MB. Returns transcript, language, and duration.

Instructions

Transcribe audio from any publicly accessible URL using OpenAI Whisper. Supports mp3, mp4, m4a, wav, webm, ogg, flac, and wma up to 24 MB. Returns the full transcript text, detected language, and estimated duration in seconds. Optionally accepts an ISO 639-1 language hint to improve accuracy. Useful for processing voice memos, meeting recordings, podcast snippets, interview clips, and audio attached to social media. Undercuts orbisapi.com audio-transcription-api by 24%.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoPublic URL of the audio file to transcribe (mp3, mp4, m4a, wav, webm, ogg, flac, wma). Must be directly accessible without authentication. Max 24 MB.
languageNoOptional ISO 639-1 language code hint (e.g. 'en', 'es', 'fr', 'de', 'ja'). Improves accuracy when the audio language is known. Omit to auto-detect.
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the use of Whisper, supported formats, size limit, and output fields. However, it omits important behavioral traits like processing time, error handling for large or inaccessible files, and model version.

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 a single paragraph of about five sentences, front-loading the main purpose. It is mostly concise, though the pricing comparison line may be slightly extraneous for tool selection.

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

Completeness3/5

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

Given two parameters and no output schema, the description covers core functionality, output structure, and use cases. However, it lacks details on error scenarios and performance expectations, leaving some gaps for an agent.

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 baseline is 3. The description reiterates the language hint and URL requirements but adds no new meaning beyond the schema descriptions.

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?

The description specifies the verb 'transcribe', the resource 'audio', and the method 'OpenAI Whisper'. It clearly states what the tool does and lists supported formats and a size limit. However, it does not explicitly differentiate from sibling tools, though none appear to offer transcription.

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?

The description provides clear use cases such as 'voice memos, meeting recordings, podcast snippets' and notes the option to provide a language hint. However, it lacks explicit guidance on when not to use the tool or mention of alternatives.

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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