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transcribe

Convert base64-encoded audio to readable text with Whisper, choosing model size and file suffix to match the audio format for improved accuracy.

Instructions

Transcribe a base64-encoded audio file using Whisper.

Args: audio_b64: Base64-encoded audio file content model: Whisper model to use: tiny, base, small, medium, large (default: base) suffix: File extension hint for the audio format, e.g. .mp3, .wav, .m4a (default: .mp3)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNobase
suffixNo.mp3
audio_b64Yes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries full burden for behavioral disclosure. It mentions the use of Whisper and parameter details, but does not disclose potential side effects, latency, output format specifics (though an output schema exists), or error conditions. It is adequate but lacks deeper behavioral context. No contradictions with annotations.

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?

The description is front-loaded with a one-line summary, followed by a neatly formatted 'Args' list. Every sentence earns its place, with no irrelevant information. The structure is easy to parse and appropriately sized for the tool's complexity.

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?

For a simple transcription tool, the description covers the essential details: what it does, input requirements, and parameter semantics. Since an output schema is present, return values need not be explained. Minor gaps exist around limitations (e.g., file size, accuracy), but overall it is sufficiently complete for an agent to use correctly.

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

Parameters5/5

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

The schema has no descriptions (0% coverage), so the description fully compensates by providing detailed explanations for all three parameters. It lists accepted values for 'model' (tiny, base, small, medium, large) with a default, and provides examples for 'suffix' (.mp3, .wav, .m4a) with a default. This adds significant meaning beyond the schema.

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?

The description clearly states the tool's function: 'Transcribe a base64-encoded audio file using Whisper.' The verb 'transcribe' is specific, and the resource (audio file) and method (Whisper) are identified. Although no siblings are provided, the purpose is unambiguous.

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 implies usage by specifying the input format (base64-encoded audio) and the Whisper model, giving an agent enough context to invoke it for transcription tasks. However, it does not explicitly mention when not to use it or compare to alternatives, as there are no siblings. The constraints are clear but not exhaustive.

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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