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Transcribe

transcribe

Transcribes audio from videos or audio files using Whisper. Returns full text with segment timestamps for aligning narration with video edits.

Instructions

Transcreve o áudio de um vídeo ou arquivo de áudio usando Whisper local. Retorna o texto completo com timestamps por segmento — essencial para alinhar narração/VO com b-rolls num fluxo de edição. Modelos disponíveis: tiny, base, small (padrão), medium, large-v3.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
video_pathYesCaminho absoluto do vídeo ou áudio (mp4, mp3, wav, m4a...)
modelNoModelo Whisper. small é um bom equilíbrio velocidade/qualidade.small
languageNoCódigo de idioma (ex: 'pt', 'en'). Se omitido, Whisper detecta automaticamente.
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses use of local Whisper, return format with timestamps, and available models. However, it does not mention file size limits, processing time, error handling, or input validation.

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?

Two sentences, front-loaded with purpose, no wasted words. Every sentence adds value.

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?

Description explains return format (full text with timestamps) and model options, sufficient for a simple transcription tool without output schema. Missing details on handling large files or errors, but adequate for typical use.

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 coverage is 100% with parameter descriptions. Description adds minimal extra meaning beyond schema (e.g., model default, usage context). Baseline 3 applies.

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?

Description clearly states it transcribes audio from video/audio files using local Whisper, returns full text with per-segment timestamps, and mentions its use in editing workflows. Distinct from sibling tools which focus on other video editing tasks.

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?

Provides context for use in aligning narration with b-rolls, but does not explicitly state when not to use or mention alternatives. Context is clear but lacks 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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