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luma_generate_video_from_image

Generate AI videos using images as start and end frames. Animate static photos, create transitions between two images, or control video content with precise frame references.

Instructions

Generate AI video using reference images as start and/or end frames.

This allows you to control the video by specifying what the first frame
and/or last frame should look like. Luma will generate smooth motion between them.

Use this when:
- You have a specific image you want to animate
- You want to create a video transition between two images
- You need precise control over the video's visual content

At least one of start_image_url or end_image_url must be provided.

Returns:
    Task ID and generated video information including URLs, dimensions, and thumbnail.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video motion and content. Describe what should happen in the video, how objects should move, what transitions to include.
start_image_urlNoURL of the image to use as the first frame of the video. The video will animate from this image.
end_image_urlNoURL of the image to use as the last frame of the video. The video will animate towards this image.
aspect_ratioNoVideo aspect ratio. Usually should match your input image ratio.16:9
loopNoIf true, generate a looping video. Default is false.
enhancementNoIf true, enable clarity enhancement. Default is true.
timeoutNoTimeout in seconds for the API to return data. Default is 300.
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?

With no annotations, description carries full burden. It explains the motion generation behavior ('smooth motion between them') and mentions Task ID return (implying async), but omits operational details like polling requirements, rate limits, or error handling that would be necessary for safe invocation.

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?

Excellent structure: purpose statement, mechanism explanation, bullet-pointed usage scenarios, constraint clarification, and return value summary. No redundant text; every sentence provides distinct value beyond the schema.

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?

Comprehensive for the tool's complexity: covers usage scenarios, input constraints, and return values (Task ID, URLs, dimensions). Minor gap: could explicitly mention the async workflow and reference luma_get_task for polling given the sibling tools available.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 100% schema coverage (baseline 3), the description adds critical semantic constraint that at least one image URL is required—a validation rule not expressed in the JSON schema (which allows empty defaults). It also adds conceptual context about controlling video via first/last frames.

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?

Description opens with specific verb+resource ('Generate AI video') and immediately clarifies the unique mechanism ('using reference images as start and/or end frames'), clearly distinguishing it from sibling text-to-video or video-extension tools.

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

Usage Guidelines5/5

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

Explicit 'Use this when:' section lists three distinct scenarios (animate specific image, create transitions, precise visual control). Also states critical constraint 'At least one of start_image_url or end_image_url must be provided,' which prevents invalid invocations.

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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