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nanobanana_edit_image

Edit or combine images using AI based on text prompts. Modify existing images, perform virtual try-ons, place products in scenes, or change attributes like materials and colors.

Instructions

Edit or combine images using AI based on a text prompt.

This allows you to modify existing images or combine multiple images together.
Perfect for virtual try-on, product placement, image enhancement, and more.

Use this when:
- You want to combine multiple images (e.g., person + clothing)
- You want to modify an existing image
- You need virtual try-on (putting clothes on a person)
- You want to place products in different scenes
- You need to change attributes (materials, colors, styles)

Common use cases:
- Portrait replacement: Try different clothing on same person
- Product scene composition: Place products in realistic environments
- Attribute replacement: Change materials, colors, or variants
- Poster editing: Rapidly change styles or themes
- 2D to 3D conversion: Convert images to 3D product mockups
- Image restoration: Restore old or damaged photos

Returns:
    Task ID, trace ID, and edited image URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the edit to perform. Describe how the images should be combined or modified. Example: 'let this person wear this T-shirt', 'place this product in a modern kitchen scene'
image_urlsYesList of image URLs to edit. Can be HTTP/HTTPS URLs (publicly accessible) or Base64-encoded images (data:image/png;base64,...). When combining multiple images, describe their relationship in the prompt.
callback_urlNoOptional webhook URL to receive the result asynchronously. The API will POST the result to this URL when complete.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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 key behaviors: the tool performs AI-based image editing/combination, supports multiple image inputs, handles both URLs and Base64 data (implied from schema), and returns a Task ID, trace ID, and image URL. It also mentions asynchronous processing via callback_url. However, it lacks details on rate limits, error conditions, or specific permissions required.

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 well-structured with clear sections (purpose, usage guidelines, common use cases, returns) and front-loaded key information. However, it includes some redundancy (e.g., 'Common use cases' partially overlaps with 'Use this when:') and could be more concise by merging similar points, though all sentences contribute meaningful context.

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

Completeness5/5

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

Given the tool's complexity (AI-based image editing with multiple inputs), no annotations, and an output schema (implied by 'Returns' section), the description is highly complete. It covers purpose, usage scenarios, behavioral aspects, and return values, providing sufficient context for an agent to understand when and how to invoke the tool effectively without relying on structured fields.

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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema by briefly mentioning 'text prompt' and 'multiple images' in the opening, but does not provide additional syntax, format, or usage details for parameters. The baseline score of 3 is appropriate as the 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 tool's purpose with specific verbs ('edit or combine images using AI based on a text prompt') and distinguishes it from sibling tools like 'nanobanana_generate_image' by focusing on modifying existing images rather than generating new ones from scratch. It explicitly mentions the resource ('images') and the AI-driven mechanism.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool through a dedicated 'Use this when:' section listing five specific scenarios (e.g., combining multiple images, modifying an image, virtual try-on). It also distinguishes from alternatives by implying this is for editing existing images, unlike 'nanobanana_generate_image' for generation from scratch.

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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