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transcribe

Transcribe audio files to text with word-level timestamps using Whisper, enabling precise analysis and debugging of voice recordings.

Instructions

Transcribe an audio file to text with word-level timestamps using Whisper

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoLanguage code (default: en)en
audio_pathYesPath to the audio file (.wav, .mp3, .m4a)
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosure. It does state the output type ('text with word-level timestamps') and the engine ('Whisper'), but it fails to mention side effects, permissions, network requirements, or whether the tool is read-only. For a tool that processes files, this lack of safety/behavioral detail is a significant gap.

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 a single, front-loaded sentence with no unnecessary words. It efficiently conveys the action, output details, and underlying model, making it highly concise and well-structured.

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 the tool's simplicity (2 parameters, no output schema), the description covers the core purpose and output. However, it does not specify the exact return format (e.g., whether it returns a plain text string with timestamps or a structured object), and with no output schema, this ambiguity could confuse an agent. It is not fully complete for invocation but is adequate for a basic tool.

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%, with both 'audio_path' and 'language' described in the schema. The description adds no additional parameter meaning, so the baseline score of 3 applies. It neither enhances nor detracts from the schema's clarity.

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 verb and resource: 'Transcribe an audio file to text with word-level timestamps using Whisper'. It is specific about the output (text with word-level timestamps) and the method (Whisper), distinguishing it from sibling tools like quality_score and compare_tts which focus on evaluation and comparison, not transcription.

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 description implies usage: when you need to convert an audio file to text, use this tool. However, it does not explicitly state when to use it vs. alternatives, nor any exclusions or prerequisites. No mention of sibling tools or scenarios is provided.

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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