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veo_text_to_video

Generate AI videos from text descriptions using Google's Veo technology. Create custom video clips by describing scenes, subjects, actions, and styles in your prompt.

Instructions

Generate AI video from a text prompt using Veo.

This creates a video from scratch based on your text description. Veo
will interpret your prompt and generate a matching video clip.

Use this when:
- You want to create a video from a text description
- You don't have a reference image to use
- You want maximum creative freedom for Veo

For video generation starting from an image, use veo_image_to_video instead.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video to generate. Be descriptive about scene, subject, action, camera movement, lighting, and style. Examples: 'A white ceramic coffee mug on a glossy marble countertop, steam rising, soft morning light', 'Cinematic drone shot over a forest at sunset, golden hour lighting'
modelNoVeo model version. 'veo2' for quality mode, 'veo2-fast' for faster generation. 'veo3'/'veo31' offer improved quality. Models with '-fast' suffix are faster but slightly lower quality.veo2
aspect_ratioNoVideo aspect ratio. '16:9' for landscape/widescreen, '9:16' for portrait/vertical, '1:1' for square, '4:3' for standard, '3:4' for portrait standard.16:9
translationNoIf true, automatically translate the prompt to English for better generation quality. Useful for non-English prompts.
resolutionNoVideo resolution. Options: '4k' for highest quality, '1080p' for standard HD, 'gif' for animated GIF format. If not specified, uses the model's default resolution.
callback_urlNoOptional URL to receive a POST callback when generation completes. The callback will include the task_id and video results.

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 of behavioral disclosure. It effectively describes the core behavior ('creates a video from scratch based on your text description') and return values ('Task ID and generated video information including URLs and state'). However, it lacks details on potential limitations like rate limits, processing time, or error conditions, which would be helpful for a generative AI tool.

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 well-structured and front-loaded with the core purpose. Each sentence earns its place: the opening statement defines the tool, the bulleted list provides clear usage guidelines, the alternative tool is mentioned, and return values are specified. There is no wasted text, and the information is organized for quick comprehension.

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 tool's complexity (generative AI video creation) and the presence of an output schema (which covers return values), the description is mostly complete. It explains what the tool does, when to use it, and distinguishes from siblings. However, for a tool with no annotations and significant behavioral implications (like video generation), adding more context on limitations or best practices would enhance completeness.

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 the schema already documents all parameters thoroughly. The description does not add any parameter-specific information beyond what's in the schema. This meets the baseline expectation when schema coverage is high, but doesn't provide extra value like explaining parameter interactions or advanced usage tips.

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's purpose with specific verb ('Generate AI video') and resource ('from a text prompt using Veo'). It explicitly distinguishes from sibling veo_image_to_video by emphasizing 'from scratch' and 'no reference image,' making the differentiation unambiguous.

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?

The description provides explicit usage guidelines with a bulleted list of when to use this tool ('when you want to create a video from a text description,' 'don't have a reference image,' 'want maximum creative freedom'). It also names the alternative tool ('use veo_image_to_video instead') for video generation starting from an image, offering clear decision 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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