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WanMCP

by AceDataCloud
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Python
Hybrid

wan_generate_video_from_image

Generate AI videos from a reference image by describing motion and content. Choose from standard, fast, or reference video models for tailored video creation.

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

Tool Definition Quality

A3.7/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only mentions the return type (Task ID and video info) but does not disclose whether the operation is asynchronous, destructive, requires authentication, rate limits, or other behavioral traits. The description adds minimal value beyond the schema.

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 with two short paragraphs and a bullet list, front-loading the purpose. Every sentence contributes meaning without redundancy or filler. It is appropriately sized for a tool with a well-documented schema.

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 the complexity (14 parameters, output schema exists), the description adequately states the purpose and return type, but lacks completeness it could mention that the tool is asynchronous, how to poll for task result, or prerequisites. The output schema covers return values, but overall context is minimally sufficient.

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?

Since schema description coverage is 100% (all 14 parameters have descriptions), the baseline is 3. The description adds little beyond the schema: it lists the three models briefly, but schema already provides defaults and enums. No additional parameter semantics are provided.

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 verb 'Generate', the resource 'AI video from a reference image', and specifies the Wan image-to-video model family. It distinguishes three model variants with brief descriptions, making the tool's purpose unambiguous even among siblings like wan_generate_video (likely text-to-video).

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 context that the tool uses reference images and lists three model options with their uses, but does not explicitly compare with sibling tools or state when not to use it. The guidance is clear for the intended use case, missing only exclusions or 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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