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sora_generate_video_async

Generate AI videos asynchronously with a callback notification. Submit a prompt and receive the result via a webhook URL 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?

Describes the async behavior, callback notification, and return of a task ID. With no annotations, it covers key behavioral traits but lacks details on error handling, timeouts, or rate limits.

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?

Well-structured with clear sections, but a couple of sentences are slightly redundant (e.g., 'This is useful for long-running video generation tasks'). Could be tightened without losing clarity.

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 the complexity (7 params, 4 enums, output schema exists), the description adequately covers purpose and usage. It could mention the callback format or error scenarios, but output schema fills some gaps.

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 the baseline is 3. The description does not add meaningful context beyond the schema's parameter descriptions; it merely restates them.

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 it generates an AI video asynchronously with callback notification. It distinguishes from sibling synchronous tools 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?

Explicitly lists use cases such as avoiding wait, having a webhook endpoint, or integrating with async workflows. However, it does not explicitly state when not to use it or directly compare to synchronous 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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