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sora_generate_video_from_image

Bring static images to life by generating AI videos from reference images. Provide image URLs and a motion prompt to create animated clips.

Instructions

Generate an AI video from reference images using Sora (Image-to-Video).

This allows you to animate or create videos based on provided images.
The AI will use the images as visual references for the generated video.

Use this when:
- You have reference images you want to animate
- You want the video to match a specific visual style
- You want to bring static images to life

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video to generate based on the image. Describe the action or motion you want to see.
image_urlsYesList of reference image URLs to use for video generation. Can be image URLs or Base64 encoded images.
modelNoSora model version. 'sora-2' or 'sora-2-pro' for higher quality.sora-2
sizeNoVideo resolution. 'small' for lower resolution, 'large' for higher resolution.large
durationNoVideo duration in seconds. Options: 10, 15, or 25 (25 only for sora-2-pro).
orientationNoVideo orientation. 'landscape', 'portrait'.landscape

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 carries full burden. It mentions the return format (Task ID, URLs, state) but lacks info on rate limits, authentication, or side effects. Adequate but not rich.

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?

Description is well-structured: first paragraph states core function, second paragraph explains usage, and last line summarizes return. Every sentence earns its place, no fluff.

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?

Given 6 parameters, 2 required, 4 enums, and an output schema (mentioned in description), the description covers purpose, usage, and returns. Could add more detail on async behavior or limitations, but 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?

Schema description coverage is 100%, so baseline is 3. Description adds high-level context but no extra meaning beyond what the schema already provides.

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 clearly states 'Generate an AI video from reference images using Sora (Image-to-Video)' and explains it animates images, distinguishing from siblings like sora_generate_video which likely doesn't use images.

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 explicit 'Use this when:' list with three clear scenarios, but does not explicitly mention when not to use or alternatives. Still offers clear guidance on usage context.

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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