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transcribe_audio

Transcribe speech from audio/video files into timestamped text locally. Prefers embedded subtitles, returns JSON with text and source.

Instructions

Speech-to-text (FunASR SenseVoice, with timestamps; embedded subtitle tracks are preferred when available). Returns JSON: {"text": ..., "source": "asr"|"subtitle"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoauto/zh/en/yue/ja/koauto
mediaYesAudio/video file path / URL
asr_modelNosensevoice
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that it uses SenseVoice, includes timestamps, prefers embedded subtitles when available, and returns a JSON with 'text' and 'source' fields. This is meaningful behavioral context, though it does not cover failure modes or rate limits.

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 two sentences, front-loaded with the core function, and the second sentence provides the return format. Every word earns its place; there is no wasted text.

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?

Considering the complexity (3 params, no output schema, no annotations), the description gives the key output format and subtitle preference, which helps the agent understand the tool's behavior. Minor gaps remain: no clarification of how 'transcribe' differs, and no details on timestamps format or error handling. Overall, it is sufficiently complete for basic invocation.

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 67% (lang and media have descriptions; asr_model only has enum values). The description adds minimal parameter context by mentioning SenseVoice (the default model) and timestamps, but it does not explain asr_model options or lang choices beyond what the schema already provides. Baseline 3 is appropriate.

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 clearly states the tool performs speech-to-text (FunASR SenseVoice, with timestamps), which is a specific verb and resource. It also mentions the output format, but it does not distinguish itself from the sibling tool 'transcribe', which could be ambiguous.

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 tool implies usage for audio/video files needing transcription and notes that embedded subtitle tracks are preferred when available, hinting at when subtitles might be used. However, it does not explicitly state when to use this tool over the 'transcribe' sibling or provide any exclusions.

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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