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sora_generate_video_async

Generate AI videos asynchronously by providing a prompt and a callback URL; receive a POST request with the video result when generation completes.

Instructions

Generate an AI video asynchronously with callback notification.

This is useful for long-running video generation tasks. Instead of waiting
for the video to complete, you'll receive a callback at your specified URL
when the generation is finished.

Use this when:
- You don't want to wait for the generation to complete
- You have a webhook endpoint to receive results
- You're integrating with an async workflow

The callback will receive a POST request with the same response format
as the synchronous generation tools.

Returns:
    Task ID that you can use to correlate with the callback.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video to generate.
callback_urlYesURL to receive the callback when video generation is complete. The result will be POSTed to this URL as JSON.
modelNoSora model version.sora-2
sizeNoVideo resolution.large
durationNoVideo duration in seconds.
orientationNoVideo orientation.landscape
image_urlsNoOptional list of reference image URLs for image-to-video generation.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses async behavior, callback POST with response format, and return of Task ID. Does not mention potential failures or retry behavior, but covers key operational details.

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, well-structured with a lead sentence, a usage section with bullet points, and a return statement. Every sentence adds value with no fluff.

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 7 parameters and existence of output schema, the description covers async behavior, callback, and task ID. It does not explicitly differentiate from siblings like sora_generate_video_v2_async, but is otherwise complete for the tool's complexity.

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%, so baseline is 3. The description does not add significant new semantics beyond what the schema provides. The callback_url parameter is explained in context of async, but this is minimal enhancement.

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's purpose: 'Generate an AI video asynchronously with callback notification.' It distinguishes itself from synchronous siblings like sora_generate_video by emphasizing the async nature and callback mechanism.

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 provides explicit 'Use this when' bullet points (don't want to wait, have webhook, async workflow). It lacks explicit 'when not to use' or direct sibling comparisons, but the guidance is clear and actionable.

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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