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image_to_image_create

Transform and edit an image using reference images and a text prompt. Choose between nano-banana and nano-banana-pro models.

Instructions

Transform and edit an existing image using reference images and a text prompt. Models: 'nano-banana' or 'nano-banana-pro'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ai_modelYesAI model to use
promptYesText description of the desired transformation
reference_image_urlsYesArray of 1-5 reference image URLs (jpg, jpeg, png)
generate_multi_viewNoGenerate multi-angle views
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions models but does not disclose whether the operation is destructive, the output format, processing time, or error handling. Basic transparency is present but insufficient for understanding side effects or non-obvious behaviors.

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 with two sentences, no redundant information, and the purpose is stated upfront. Every word serves a purpose.

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?

Despite having 4 parameters and no output schema, the description omits crucial context such as whether the tool returns a URL, a task ID, or if it runs asynchronously. Given the presence of a sibling 'wait_for_task', async behavior is likely but unmentioned. The description is incomplete for an agent to reliably invoke the tool.

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 the descriptions in the schema already explain all parameters. The tool description adds no new meaning beyond enumerating the models, which are already defined in the enum. It merely summarizes the schema, providing no additional semantic value.

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 tool transforms and edits an existing image using reference images and a text prompt. It specifies the available models, which helps distinguish from text-to-image tools. However, it does not explicitly differentiate from sibling tools like 'image_to_3d_create' or 'retexture_create'.

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

Usage Guidelines3/5

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

The description implies usage for editing images with reference images and prompts, but provides no explicit guidance on when not to use this tool or mention alternatives. The context from sibling names helps, but the description itself lacks such direction.

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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