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caption_video

Add word-timed captions to videos from transcript data, with optional karaoke word highlighting, using local ffmpeg.

Instructions

Burn styled, social-style captions into a video from a word-timed transcript — local ffmpeg, no credits. The usual chain is transcribe -> caption_video: run transcribe on the video (or its voiceover) to get word timestamps, then pass those here. Captions are styled and positioned with a font bundled in the package (no system-font dependency); optional karaoke highlights each word as it is spoken. Timestamps are relative to the video's own audio (t=0). Returns the output file path with its duration, resolution, and size, or a structured error with a hint. Requires ffmpeg. Set dry_run=true to preview without rendering.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNoOptional caption styling.
videoYesThe video to caption — a local file path (e.g. a video_generate `path`) or an http(s) video URL.
outputNoOptional output file path. Omit to write a default filename into the media output directory.
dry_runNoIf true, return the planned output and line count; run no ffmpeg.
transcriptYesThe words to show, in order — each an object with the word text and its timing in seconds. This is exactly the `words` list transcribe returns.
Behavior4/5

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

With no annotations, the description carries full disclosure burden. It reveals key behaviors: local ffmpeg (no credits), bundled font (no system dependency), karaoke optional, timestamp base (t=0 relative to video audio), return format (path, duration, resolution, size, or structured error), and the dry_run option. This is rich and transparent, though it does not cover potential edge cases like file overwrite behavior.

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 several sentences but each adds essential information: purpose, usage chain, font handling, karaoke, timestamps, return format, ffmpeg requirement, and dry_run. It is front-loaded with the primary purpose and concise without fluff. A slightly better structure could group related details, but it remains efficient.

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

Completeness5/5

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

Given the tool's moderate complexity (5 parameters, nested style object), no output schema, and no annotations, the description covers the entire workflow: prerequisite chain, input specifics, styling behavior, output details, error handling, and a preview mechanism. It is sufficiently complete for an agent to correctly invoke the tool.

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

Parameters4/5

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

The input schema already has 100% coverage of parameter descriptions, so the baseline is 3. The description adds value by linking the transcript parameter to transcribe's output ('exactly the `words` list transcribe returns') and by clarifying the video input can be a video_generate path or URL. This contextual addition justifies a 4.

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 uses a specific verb+resource combination: 'Burn styled, social-style captions into a video' and clearly identifies the input as 'word-timed transcript'. It distinguishes from siblings by emphasizing the captioning function and the local ffmpeg/no-credits aspect, which is unique among the sibling tools.

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 explicitly provides the usage chain 'transcribe -> caption_video' and explains that the transcript should come from transcribe, giving clear context for when to use this tool. It also mentions dry_run for preview without rendering, though it does not explicitly list when not to use it or suggest alternatives. The guidance is adequate 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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