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nano_banana_generate_image

Generate images from text prompts or edit existing images by providing paths alongside a prompt.

Instructions

Generate or edit images with Nano Banana.

Pass only prompt for text-to-image generation. Pass image_paths plus prompt for image editing / image-to-image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNogemini-3.1-flash-image-preview
promptYes
image_sizeNo
output_dirNo
image_pathsNo
aspect_ratioNo
include_textNo
enable_web_searchNo
request_timeout_sNo
enable_image_searchNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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 explains the modal behavior (generation vs editing) but does not mention side effects (e.g., writing to output_dir), rate limits, auth requirements, or what happens with optional flags like web search. This is useful context but lacks depth.

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 extremely concise: two short paragraphs that front-load the primary purpose and immediately provide usage modes. Every sentence adds value, with no filler or repetition. This is an exemplar of concise tool documentation.

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

Completeness2/5

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

Given the tool's complexity (10 parameters, multiple execution modes, and an output schema), the description is incomplete. It adequately explains the two invocation modes but does not guide the agent on how to choose models, when to set aspect_ratio or image_size, or what the optional boolean flags do. The presence of an output schema reduces the need to explain return values, but the tool's parameter richness demands more guidance than this description provides.

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?

With schema_description_coverage at 0%, the description must compensate for the 10 parameters. It only explains the role of 'prompt' and 'image_paths', leaving the other eight parameters (model, image_size, aspect_ratio, include_text, enable_web_search, request_timeout_s, enable_image_search, output_dir) without any descriptive benefit beyond their schema titles. The two explained parameters are helpful, but the mismatch is significant.

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 or edit images with Nano Banana', which clearly states the tool's action (generate/edit) and resource (images). It further distinguishes two modes (text-to-image vs image editing) and is unambiguous when compared to the sibling 'nano_banana_models'.

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 explicitly instructs when to pass only 'prompt' versus 'image_paths' plus 'prompt', giving clear context for each invocation mode. However, it does not mention when to prefer an alternative tool or explicitly exclude certain scenarios, so it stops short of a perfect 5.

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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