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wan_generate_video_from_image

Turn a reference image into an AI video by describing desired motion and content. Supports multiple models for standard or faster generation.

Instructions

Generate AI video from a reference image using Wan image-to-video models.

This supports three models:
- wan2.6-i2v: Standard image-to-video generation
- wan2.6-r2v: Reference video-to-video with character/timbre extraction
- wan2.6-i2v-flash: Fast image-to-video generation

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video motion and content. Describe what should happen in the video.
image_urlYesURL of the reference image for video generation. The video will be generated based on this image.
modelNoModel to use. Options: 'wan2.6-i2v' (standard image-to-video), 'wan2.6-r2v' (reference video-to-video), 'wan2.6-i2v-flash' (fast image-to-video). Default: 'wan2.6-i2v'.wan2.6-i2v
negative_promptNoContent to exclude from the video. Maximum 500 characters.
durationNoVideo duration in seconds. Options: 5, 10, or 15.
resolutionNoVideo resolution. Options: '480P', '720P' (default), '1080P'.720P
reference_video_urlsNoComma-separated URLs of reference videos for character/timbre extraction. Used with wan2.6-r2v model.
shot_typeNoShot type: 'single' for continuous shot, 'multi' for multi-cut editing.
audioNoWhether the generated video should include audio. Default is false.
audio_urlNoURL of reference audio to use in the video.
prompt_extendNoEnable LLM-based prompt rewriting. Default is false.
sizeNoThe size of the generated video (e.g., '1280x720').
timeoutNoTimeout in seconds. Default is 1800.
callback_urlNoWebhook callback URL for asynchronous notifications.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries full burden. It discloses that video is generated and returns task info, but does not mention potential issues like generation time, costs, rate limits, or side effects (e.g., data usage). The description lacks behavioral context beyond basic functionality.

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?

The description is relatively concise and structured. It starts with a clear statement, lists models, and mentions returns. Each sentence adds value, though the returns section could be considered redundant if output schema exists. Front-loading is acceptable.

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?

Given 14 parameters, high schema coverage, and an output schema (though not shown), the description adequately covers core functionality and returns. However, it lacks guidance on model selection and prerequisites (e.g., image URL accessibility). It is functional but not fully comprehensive.

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%, with each parameter having a clear description. The description adds context about the three models but does not significantly enhance understanding of parameter semantics beyond what the schema already provides. Baseline 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?

The description clearly states the tool generates AI video from a reference image using specific models, and lists the three models. It distinguishes from sibling tools (e.g., wan_generate_video for text-to-video) by focusing on image-to-video. The return type is also mentioned.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide guidance on when to use this tool versus alternatives (e.g., wan_generate_video). It lists models but does not explain which scenario each model is best for. No when-not-to-use or prerequisite conditions are given.

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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