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veo_generate_video

Generate videos from text prompts or images using Google Veo models. Starts a background generation process and returns a session ID to track progress.

Instructions

Generate videos using Google's Veo models.

Supports:

  • Text-to-video: Provide a text prompt (all models)

  • Image-to-video: Provide an image path (Veo 3 only)

  • Both: Provide both image and prompt for guided animation (Veo 3 only)

This tool starts a background video generation process that:

  1. Initiates video generation with the specified parameters

  2. Monitors progress in the background

To download generated videos, use veo_download_video after generation completes.

Returns:

  • session_id: Use with veo_check_generation to monitor progress

  • status: Current generation status

  • pid: Process ID of the background worker

Examples: Text-to-video: "Serene waterfall in a lush forest, cinematic lighting" Image-to-video: Use image_path="/path/to/image.jpg" with optional prompt

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fpsNoFrames per second for video generation
seedNoSeed for reproducible generation
modelNoVeo model to use for generation. veo-3.0-fast optimizes for speed.veo-3.0-fast-generate-preview
promptNoText prompt describing the video to generate. Be specific about visual elements, style, and movement. Required for text-to-video, optional for image-to-video.
image_pathNoPath to input image for image-to-video generation (Veo 3 only). The image becomes the first frame of the video. Max 20MB.
resolutionNoVideo resolution (if supported by model)
aspect_ratioNoVideo aspect ratio (SDK supports 16:9 and 9:16)16:9
enhance_promptNoLet the model enhance your prompt for better results
generate_audioNoGenerate audio for the video
output_gcs_uriNoGCS bucket where to save the generated videos
negative_promptNoElements to avoid in the generation (e.g., 'low quality, blurry')
duration_secondsNoVideo duration in seconds (2-15). Not supported by veo-3.0-fast model. SDK uses model default if not specified.
number_of_videosNoNumber of video variations to generate (1-4)
person_generationNoControl person generation in videos (SDK supports dont_allow, allow_adult)allow_adult

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are present, so the description carries the full burden. It discloses the asynchronous nature ('starts a background video generation process'), returns (session_id, status, pid), and model-specific constraints (image-to-video only Veo 3). It does not mention rate limits or failure modes, but the critical behavioral traits are covered.

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 with clear headings and bullet points, making it scannable. Each section (Supports, workflow, Returns, Examples) adds unique, non-redundant information, and there is no filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (14 parameters, async workflow), the description covers the full lifecycle: generation, monitoring via session_id, and download via veo_download_video. It names companion tools, explains return values, and provides examples. It is self-sufficient for an agent to select and invoke the tool correctly.

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?

The schema has 100% parameter coverage, so baseline is 3. The description adds value beyond the schema by explaining how prompt and image_path combine for the three modes, providing a concrete example ('Serene waterfall in a lush forest, cinematic lighting'), and noting that image_path is Veo 3 only—a constraint not fully explicit in the schema.

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 opens with 'Generate videos using Google's Veo models,' a clear verb+resource statement. It further distinguishes itself from sibling tools (veo_check_generation, veo_download_video) by being the generation entry point and enumerating the three supported modes (text-to-video, image-to-video, both), making its scope unambiguous.

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?

It provides clear workflow context: 'This tool starts a background video generation process' and directs users to 'use veo_download_video after generation completes' and reference veo_check_generation for monitoring. This explains how the tool fits into the pipeline, though it does not explicitly state when not to use it or name alternative generation tools.

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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