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Generate Image

generate_image

Generate images from text prompts using multiple AI providers. Images are saved to disk and file paths returned.

Instructions

Generate images using multiple providers (Google Gemini, OpenAI, BFL FLUX, Reve). Images are saved to disk and the file paths are returned.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoResponse mode. image returns only the image; image_and_text also returns a description (Google models only)image
modelNoModel to use. Available models depend on configured API keysgpt-image-2
promptYesText prompt describing the image to generate
thinkingNoControls model thinking before generation (Google models only). none disables thinking; auto lets the model decideauto
outputDirNoDirectory path where generated images will be saved. If NANO_BANANA_OUTPUT_DIR is set, relative paths are resolved from that base and all paths are sandboxed within it..
resolutionNoOutput image resolution. Higher values may not be supported by all models1K
aspectRatioNoAspect ratio of the generated image1:1
inputImagesNoFile paths of images to include as input alongside the prompt (supports PNG, JPEG, WEBP, GIF). Supported by Google models, OpenAI gpt-image models (uses the images.edit endpoint) and Reve (sent as v2 references).
Behavior3/5

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

No annotations are provided, so the description carries full behavioral disclosure. It covers core behaviors (multiple providers, save to disk, return paths) and notes that mode/thinking apply only to Google models. However, it omits details like file naming, overwrite behavior, and error handling, which are important for a tool that saves 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 two sentences, front-loaded with the main purpose, and contains no unnecessary words. Every sentence provides essential information about what the tool does and its result.

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

Completeness3/5

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

For a tool with 8 parameters and no output schema, the description covers the main purpose but lacks details on output specifics (file format, naming, directory behavior beyond parameter description). The schema fills gaps but the description alone is not fully complete for end-to-end understanding.

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% with detailed descriptions for all 8 parameters. The tool description adds minimal value for parameters, only reiterating the multi-provider aspect. Baseline is 3, and no further improvement is justified.

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 images using multiple named providers, saves to disk, and returns file paths. The verb 'generate' and resource 'image' are specific, and the inclusion of provider names adds context. No sibling tools exist, so differentiation is not needed.

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?

The description lacks guidance on when to use this tool versus alternatives. No sibling tools or explicit usage contexts are provided. The description only states what the tool does, not when it should be preferred or avoided.

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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