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generate_video_from_image

Animate a still image into a video by providing a text prompt, powered by Google's Veo 2.0 model.

Instructions

Generate a video from an initial image using Google Gemini Veo 2.0

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
saveNoWhether to save the generated video to the filesystem
topKNoTop-k parameter for sampling
topPNoTop-p parameter for sampling
modelNoModel to useveo-2.0-generate-001
promptYesText description of the video to generate
temperatureNoTemperature for generation (0.0 to 1.0)
Behavior2/5

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

With no annotations provided, the description must bear the full burden of behavioral disclosure, but it only states the core action. It does not disclose what happens to the generated video (e.g., whether it is saved to the filesystem), the output format, or any required authentication or rate limits. The description also does not mention the 'save' parameter's default behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that gets to the point quickly. It is structurally efficient and easy to parse, although it may be too terse for a tool with 6 parameters and no output schema.

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

Completeness1/5

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

The description is severely incomplete for a tool with 6 parameters and no output schema. It lacks critical information: how the initial image is supplied, what the return value or output looks like, when to adjust sampling parameters, and any side effects. The absence of an image parameter in the schema further compounds this incompleteness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema covers 100% of parameters with descriptions, the tool description introduces an 'initial image' concept that is not present in the schema, creating confusion. It also does not explain the sampling parameters (topK, topP, temperature) or how they affect results, leaving the agent to rely solely on the schema without additional context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (generate a video) and the resource (from an initial image using Gemini Veo 2.0), which distinguishes it from siblings like generate_image and generate_video. However, the phrase 'from an initial image' is not reflected in the input schema, which has no image parameter, making the purpose slightly ambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no explicit guidance on when to use this tool vs. alternatives, and does not explain how to provide the 'initial image' (since no image parameter exists in the schema). It fails to mention prerequisites, exclusions, or any context for choosing this tool over generate_video.

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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