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start_video_generation

Initiate AI video generation from text prompts, reference images, or frame interpolation using Google's Veo 3.1 model. Returns an operation ID for tracking job completion.

Instructions

Start a Veo 3.1 video generation job. This returns an operation ID immediately - use get_video_job to poll for completion. Supports text-to-video, reference images (up to 3), and first/last frame interpolation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesText description of the video to generate
modelNoModel to use: veo-3.1-generate-preview (quality, $0.75/sec) or veo-3.1-fast-generate-preview (speed, $0.10/sec). Default: fast
durationSecondsNoVideo duration: 4, 6, or 8 seconds (default: 8)
aspectRatioNoAspect ratio (default: 16:9). Note: 9:16 may not work with reference images.
resolutionNoVideo resolution (default: 1080p)
seedNoOptional seed for reproducible generation
sampleCountNoNumber of videos to generate (1-4, default: 1)
generateAudioNoWhether to generate synchronized audio (default: false). Costs 2x more.
referenceImagesNoUp to 3 reference images for visual guidance. Each can be URL, file path, or fileUri.
firstFrameNoFirst frame for interpolation (must also provide lastFrame)
lastFrameNoLast frame for interpolation (must also provide firstFrame)
negativePromptNoOptional: Things to avoid in the video
resizeModeNoHow to fit reference images (default: pad)
Behavior3/5

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 asynchronous nature ('returns an operation ID immediately') and polling requirement, which is crucial. However, it lacks information about costs, rate limits, authentication needs, or error handling, which are important for a complex video generation 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 perfectly concise with three sentences that each earn their place: the core purpose, the polling requirement, and the key capabilities. It's front-loaded with the most critical information and wastes no words.

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

Completeness3/5

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

Given the complexity (13 parameters, nested objects) and lack of both annotations and output schema, the description is somewhat incomplete. While it covers the asynchronous behavior and key features, it doesn't address costs, permissions, or what the operation ID represents. For such a rich tool, more contextual guidance would be helpful.

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?

The schema description coverage is 100%, so the schema already documents all 13 parameters thoroughly. The description adds minimal value by mentioning 'reference images (up to 3)' and 'first/last frame interpolation', which are already covered in the schema. This meets the baseline of 3 when schema does the heavy lifting.

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 specific action ('Start a Veo 3.1 video generation job') and resource ('video generation job'), distinguishing it from siblings like 'extend_video' or 'start_batch_video_generation'. It also mentions the immediate return type ('operation ID') and key capabilities ('text-to-video, reference images, first/last frame interpolation').

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?

The description provides clear context on when to use this tool by stating it 'returns an operation ID immediately - use get_video_job to poll for completion', which implicitly guides the agent to follow up with the sibling tool. However, it doesn't explicitly mention when NOT to use it or alternatives like 'start_batch_video_generation' for batch jobs.

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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