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banana

Generate and edit images using AI with text prompts and reference images, supporting multiple aspect ratios, resolutions, and style transfers.

Instructions

Generate images using Nano Banana Pro (Gemini 3 Pro Image).

CAPABILITIES:

  • Text-to-image generation with high quality output

  • Image editing and transformation with reference images

  • Multiple aspect ratios and resolutions (1K/2K/4K)

  • Style transfer and multi-image fusion

  • Optional search grounding for factual content

RESPONSE FORMAT:

  • Returns XML with file paths to generated images

  • Images are saved to disk (no base64 in response)

  • Includes text descriptions and optional thinking process

BEST PRACTICES:

  • Be descriptive: describe scenes, not just keywords

  • Use negative constraints in prompt: "no text", "no watermark"

  • For editing: provide reference image and specify what to keep

  • For style transfer: provide style reference image

Supports: reference images with roles (edit_base, style_ref, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesImage generation prompt. Structure: <goal>what you want to generate (can be a statement)</goal> <context>detailed background info - the more the better</context> <hope>desired visual outcome, can be abstract</hope>. Example: <goal>Generate 6 weather icons for a mobile app</goal> <context>Target users are young professionals, app has a friendly casual vibe, needs to match existing UI with rounded corners</context> <hope>pastel colors, consistent 3px stroke, 64x64 base size</hope>
imagesNoReference images for editing or style transfer. Roles: edit_base (image to edit), subject_ref (person/character), style_ref (style reference), layout_ref (layout), background_ref, object_ref.
aspect_ratioNoOutput image aspect ratio. Default: 1:1 (square).1:1
resolutionNoOutput resolution. 1K (1024px), 2K (2048px), 4K (4096px). Default: 4K.4K
use_searchNoEnable search grounding for factual content. Adds text to response.
include_thoughtsNoInclude model's thinking process in response.
temperatureNoControls randomness (0.0-2.0). Higher = more creative. Default: 1.0.
top_pNoNucleus sampling threshold (0.0-1.0). Default: 0.95.
top_kNoTop-k sampling (1-100). Default: 40.
num_imagesNoNumber of images to generate (1-4). Default: 1.
save_pathYesBase directory for saving images. Files saved to {save_path}/{task_note}/.
task_noteYesSubdirectory name for saving images (English recommended, e.g., 'hero-banner', 'product-shot'). Also shown in GUI.
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 and does well by disclosing key behaviors: 'Images are saved to disk (no base64 in response)', 'Returns XML with file paths', and mentions capabilities like style transfer and multi-image fusion. It doesn't cover rate limits or authentication needs, but provides substantial operational context.

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 well-structured with clear sections (CAPABILITIES, RESPONSE FORMAT, BEST PRACTICES) and every sentence earns its place by providing actionable information. It's appropriately sized for a complex tool without unnecessary verbosity, making it easy to scan and understand.

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 tool's complexity (12 parameters, no output schema, no annotations), the description does a strong job covering capabilities, response format, and best practices. It could benefit from more explicit error handling or performance characteristics, but provides sufficient context for effective use despite the missing 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 12 parameters thoroughly. The description adds minimal parameter-specific information beyond the schema (e.g., briefly mentions reference images with roles), but doesn't provide significant additional semantic value. This meets the baseline for high schema coverage.

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 'Generate images using Nano Banana Pro (Gemini 3 Pro Image)' which specifies the verb (generate) and resource (images) with the specific model. However, it doesn't explicitly differentiate from sibling tools like 'image' or 'gemini' which might also handle image-related tasks, preventing a perfect score.

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 'BEST PRACTICES' section provides clear guidance on when and how to use the tool effectively (e.g., 'Be descriptive', 'Use negative constraints', 'For editing: provide reference image'). It doesn't explicitly mention when NOT to use it or name specific alternatives among siblings, but the practical advice is comprehensive for proper usage.

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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