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Retake Video

xbrush_video_retake

Regenerate a video up to a given timestamp to create a new variation. Submits an async retake job using a specified model and source video URL.

Instructions

Regenerate (retake) a video up to a given timestamp, producing a new variation. Submits async — poll the returned request_id with xbrush_get_request.

Args: model (string, required): Retake model (e.g. ltx-2.3-retake). See xbrush_list_models(category='video'), featureType 'retake'. video_url (string, required): URL of the source video to retake. end_time (number, required): Timestamp in seconds (>= 0) up to which to regenerate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesVideo-retake model (e.g. ltx-2.3-retake). Use xbrush_list_models(category='video') and pick a model whose featureType is 'retake'.
end_timeYesTimestamp in seconds (>= 0) up to which the video is regenerated into a new variation.
video_urlYesURL of the source video to retake.
Behavior4/5

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

Annotations declare readOnlyHint=false and openWorldHint=true, so the mutation is expected. The description adds valuable behavioral context by stating 'Submits async — poll the returned request_id with xbrush_get_request,' which is essential for the agent to know the tool is non-blocking and how to retrieve the result.

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 concise and well-structured: a one-sentence purpose, a brief async note, and a clean Args list. Every sentence earns its place, and the most important operational detail (async + request_id) is front-loaded.

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?

Given there is no output schema, the description does enough by mentioning the returned request_id and how to poll. It covers all three parameters and the async behavior. It could add what the final output will look like, but the pointer to xbrush_get_request sufficiently bridges that gap.

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?

The input schema has 100% description coverage, so the description adds little beyond what the schema already states. It does provide a concrete model example (ltx-2.3-retake) and points to xbrush_list_models, but this is also present in the schema. There is no significant extra meaning added.

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 action: 'Regenerate (retake) a video up to a given timestamp, producing a new variation.' It specifies the resource (video) and the operation (retake), and the timestamp constraint distinguishes it from related video tools like generate, extend, or upscale.

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 gives clear context for use: you retake a video up to a given timestamp, and it explicitly notes the async workflow with xbrush_get_request. It also points to xbrush_list_models for selecting the right model, but it does not explicitly list exclusions or tell when not to use this tool versus alternatives.

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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