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edit_image

Edit existing images with natural language prompts using Google Gemini. Supports multi-image input (up to 10) and saves the edited full-resolution image to disk.

Instructions

Edit an existing image based on a text instruction using Google Gemini. Default model: gemini-3.1-flash-image-preview. Supports multi-image input (up to 10 total). Response contains a thumbnail preview; full-res image is saved to /root/nano-banana-output.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoImage size (512, 1K, 2K, 4K)
promptYesWhat to change in the image
filePathYesPath to the source image
aspectRatioNoAspect ratio of the output image
negativePromptNoThings to exclude from the edited image
systemInstructionNoSystem instruction to guide the model's behavior
additionalFilePathsNoAdditional image paths for multi-image editing (up to 9)
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. It discloses output behavior (thumbnail preview, full-res saved to /root/nano-banana-output), the multi-image limit (up to 10 total), and the default model, going beyond basic operation. It does not mention potential side effects like overwriting, but the output path suggests new files.

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 concise, front-loaded with the core purpose, and then provides specific details about model, multi-image support, and output. Every sentence adds value with no waste.

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?

Given the absence of output schema and annotations, the description sufficiently explains what the tool does and what the user gets (thumbnail preview, full-res saved to path). It is complete for a basic edit tool, though it lacks error handling details or explicit prerequisites.

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 parameters are already well-documented. The description adds context about multi-image input (up to 10 total) which maps to additionalFilePaths, but this is inferable from the schema. No significant extra semantics are provided.

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 function with a specific verb ('Edit'), resource ('existing image'), and method ('text instruction'). It distinguishes itself from siblings (generate_image, describe_image) by focusing on modification rather than generation or description.

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 implies when to use the tool ('Edit an existing image') and provides context about multi-image input and output behavior. However, it does not explicitly reference alternative sibling tools or state exclusions, so it stops short of full usage guidelines.

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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