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transcribe_and_caption

Transcribes video audio to text with word-level timing and creates ASS captions. Optionally burns captions into a new render using brand, karaoke, or minimal styles.

Instructions

ASR word timeline + ASS captions; optionally burn into a new render (brand|karaoke|minimal).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
burnNo
styleNobrand
languageNo
model_sizeNobase
project_idYes
fallback_transcriptNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description must carry full behavioral disclosure. It mentions the core actions (ASR, captioning, burning) and style options, but omits important details like whether the transcription modifies the project, if the burn overwrites existing renders, or any side effects. It partially covers transparency but has gaps.

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 well-formatted sentence that fronts the key actions and options. It could be restructured for clarity (e.g., bullet points for parameters), but it is efficient and free of redundancy.

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

Completeness2/5

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

Given the complexity (6 parameters, 0% schema coverage) and presence of an output schema (but not described), the description is insufficient. It fails to mention prerequisites, return values, or how the tool integrates into a workflow, leaving significant gaps for agent understanding.

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 0%, so the description must compensate. It adds meaning for 'burn' (optional) and 'style' (examples: brand, karaoke, minimal), but does not clarify parameters like 'language', 'model_size', or 'fallback_transcript', leaving the agent to infer their purpose from names alone.

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 performs ASR to produce a word timeline and ASS captions, and optionally burns them into a new render with style options. It effectively communicates the core functionality and distinguishes itself from sibling tools like add_graphics or process_audio.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, limitations, or competing tools, leaving the agent without context for tool selection.

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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