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generate_image

Generate images from text prompts using Imagen or Nano Banana models. Save the result to a specified file path.

Instructions

Generates an image using Imagen or Nano Banana (Gemini image) models.

Args: prompt: Text description of the image to generate. output_path: File path to save the generated image. model: Model alias or ID. "imagen" (default, ultra), "imagen-fast", "nano-pro", or "nano-flash". aspect_ratio: Aspect ratio (e.g. "1:1", "16:9", "9:16", "4:3", "3:4"). image_size: Output size for Nano Banana only (e.g. "1024x1024"). Not supported by Imagen.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesText description of the image to generate
output_pathYesFile path to save the generated image
modelNo"imagen" (default, ultra), "imagen-fast", "nano-pro", or "nano-flash"imagen
aspect_ratioNoAspect ratio (e.g. "1:1", "16:9", "9:16", "4:3", "3:4")
image_sizeNoOutput size for Nano Banana only (e.g. "1024x1024"). Not supported by Imagen

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations only include openWorldHint=true. The description explains the model options and constraints (e.g., image_size not supported by Imagen) but lacks details on error handling, rate limits, or other side effects. Minimal behavioral disclosure beyond what annotations provide.

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?

Description is well-structured with bullet-like clarity and front-loaded purpose. A few words could be trimmed, but it remains efficient.

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?

Has output schema so return format is covered. Explains all parameters and model-specific behavior. Lacks guidance on when to choose each model variant, but sufficient for basic usage.

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 coverage is 100%, so baseline is 3. The description adds some clarification (e.g., defaults, model restrictions) but does not significantly exceed the schema's own descriptions.

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 generates an image using named models (Imagen, Nano Banana). It lists key parameters and distinguishes from unrelated siblings like generate_speech, which is for audio.

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?

No explicit guidance on when to use this tool versus alternatives, or when not to use it. It provides parameter details but does not add usage context.

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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