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generate_video

Generate videos from text descriptions or image inputs using various AI models. Supports multiple resolutions, durations, and styles, returning a task ID for asynchronous retrieval of results.

Instructions

Generate a video asynchronously from text or images. Returns a task ID to poll for results. Supports multiple models: CogVideoX-3 (text/image/start-end frame), Vidu Q1 (1080P), Vidu 2 (720P, faster).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fpsNoCogVideoX-3 only: Frame rate (30 or 60)
sizeNoVideo resolution (e.g., "1920x1080", "1280x720"). Model-specific defaults apply.
modelYesVideo generation model to use
styleNoVidu Q1 text-to-video only: Style of the video
promptNoText description of the video (max 512 characters). Required for text-to-video models.
qualityNoCogVideoX-3 only: "quality" for higher quality, "speed" for faster generation
user_idNoUnique end user ID for abuse prevention (6-128 characters)
durationNoVideo duration in seconds. Model-specific: CogVideoX-3: 5 or 10, Vidu Q1: 5, Vidu 2: 4
image_urlNoImage URL(s) for image-to-video generation. Single URL or array of URLs for start-end frame/reference images.
with_audioNoWhether to generate AI sound effects (CogVideoX-3, Vidu 2)
aspect_ratioNoVidu Q1 text/reference only: Aspect ratio
movement_amplitudeNoVidu models only: Motion amplitude
Behavior3/5

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

Discloses async behavior and model differences but omits details on failure modes, timeouts, auth requirements, or rate limits. No annotations exist to compensate, so description carries full burden but falls short.

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?

Two sentences, no redundancy. Front-loaded with the core action and key details. Every word earns its place.

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

Completeness3/5

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

Adequate for a tool with thorough schema descriptions, but lacks examples or guidance on parameter combinations. No output schema means description should clarify return value further, which it does minimally.

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 100%, so the schema itself documents all parameters. The description adds minimal extra context beyond listing model capabilities, so baseline of 3 is appropriate.

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?

Clearly states it generates a video asynchronously from text or images and returns a task ID. Lists supported models, distinguishing it from sibling tools like generate_image_async and get_async_result.

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?

Indicates async nature and need to poll for results, hinting at use with get_async_result. Does not explicitly state when not to use or compare with synchronous alternatives like generate_and_download_video.

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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