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wan_generate_video

Generate AI video from a text prompt. Describe scene, motion, style, and mood, with optional settings for duration, resolution, audio, and prompt enhancement.

Instructions

Generate AI video from a text prompt using Wan text-to-video model.

This uses the wan2.6-t2v model to create video from text descriptions.
For creating video from images, use wan_generate_video_from_image instead.

Returns:
    Task ID and generated video information including URLs and state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video to generate. Be descriptive about the scene, motion, style, and mood.
negative_promptNoContent to exclude from the video. Maximum 500 characters.
durationNoVideo duration in seconds. Options: 5, 10, or 15. Default depends on model.
resolutionNoVideo resolution. Options: '480P', '720P' (default), '1080P'.720P
audioNoWhether the generated video should include audio. Default is false.
audio_urlNoURL of reference audio to use in the video. Only used when audio is enabled.
prompt_extendNoEnable LLM-based prompt rewriting for better results. Default is false.
sizeNoThe size of the generated video (e.g., '1280x720').
timeoutNoTimeout in seconds for the API to return data. Default is 1800.
callback_urlNoWebhook callback URL for asynchronous notifications. When provided, the API will call this URL when the video is generated.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description bears full burden. It mentions the model and return values but does not explicitly disclose the asynchronous nature, potential timeouts, or required polling.

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?

Three concise paragraphs with purpose, usage differentiation, and return summary. No wasted words; front-loaded with key actions.

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?

Output schema exists, so returns are covered. However, with no annotations, the description should clarify the async workflow (e.g., need to poll task) and limitations, which it does not.

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 schema already documents all parameters. Description adds minimal extra guidance (e.g., 'be descriptive' for prompt), meeting baseline for high coverage.

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 AI video from text prompts using the Wan model, and explicitly differentiates from the sibling tool for image-to-video generation.

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?

Provides clear context for when to use this tool (text-to-video) and directs to an alternative for image input, but does not address other sibling tools like wan_get_task.

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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