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luma_generate_video

Generate AI videos from text descriptions without reference images. Create high-quality video content by describing scenes, motion, and style. Supports multiple aspect ratios and looping options.

Instructions

Generate AI video from a text prompt using Luma Dream Machine.

This is the simplest way to create video - just describe what you want and Luma
will generate a high-quality AI video.

Use this when:
- You want to create a video from a text description
- You don't have reference images
- You want quick video generation

For using reference images (start/end frames), use luma_generate_video_from_image instead.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video to generate. Be descriptive about the scene, motion, style, and mood. Examples: 'A cat walking through a garden with butterflies', 'Astronauts shuttle from space to volcano', 'Ocean waves crashing on a beach at sunset'
aspect_ratioNoVideo aspect ratio. Options: '16:9' (landscape, default), '9:16' (portrait), '1:1' (square), '4:3', '3:4', '21:9' (ultrawide), '9:21'.16:9
loopNoIf true, generate a looping video where end connects seamlessly to start. Default is false.
enhancementNoIf true, enable clarity enhancement for the video. 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
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns a Task ID (implying async behavior) and describes the return payload (URLs, dimensions, thumbnail). However, it omits explicit mention of the async polling pattern or that users should use luma_get_task to check status, which is relevant behavioral context for a generation API.

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?

Perfectly structured with clear sections: purpose statement, simplicity claim, usage conditions, alternative tool reference, and return value summary. Every sentence provides distinct value without repetition or verbosity.

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 the presence of an output schema and 100% parameter coverage, the description appropriately focuses on high-level guidance. It covers purpose, differentiation from siblings, and return summary. Minor gap: does not explicitly mention luma_get_task for polling despite referencing the returned Task ID.

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%, establishing a baseline of 3. The description mentions 'text prompt' aligning with the required parameter, but adds no semantic details beyond what the schema already provides for aspect_ratio, loop, enhancement, timeout, or callback_url.

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 opening sentence 'Generate AI video from a text prompt using Luma Dream Machine' provides a specific verb, resource, and method. It clearly distinguishes from sibling tool luma_generate_video_from_image by explicitly naming it as the alternative for reference images.

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?

Provides explicit 'Use this when:' bullet points covering text descriptions, lack of reference images, and quick generation needs. Explicitly names the sibling tool luma_generate_video_from_image for image-based workflows, giving clear alternation criteria.

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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