Nano Banana MCP

by DevShiftTeam

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TypeScript

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Generate Image

generate_image

Create images from text descriptions using Google Gemini models. Get a saved file path for each generated image.

Instructions

Create a new image from a text prompt using Google Gemini image models. Returns the file path of the saved image.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "generate_image"
  }
}

Input Schema

Name	Required	Description	Default
`model`	No	Model ID to use. Options: gemini-3.1-flash-image-preview (default), gemini-2.5-flash-image, gemini-3-pro-image-preview
`prompt`	Yes	Text description of the image to generate, e.g. "a watercolor painting of a sunset over mountains"
`aspectRatio`	No	Aspect ratio of the generated image. Default: 1:1	1:1

Output Schema

Name	Required	Description
`model`	Yes	Model ID that was used for generation
`filePath`	Yes	Absolute path where the generated image was saved
`sizeBytes`	Yes	Size of the generated image in bytes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 6, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already signal that this is a mutating operation. The description adds useful behavioral context by naming the model family (Google Gemini image models), the side effect of saving the image, and the return form (file path). No contradiction with annotations exists.

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 a single, compact sentence that front-loads the core action, indicates the model provider, and states the return value. It contains no filler, repetition, or vague phrasing.

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?

For a simple generation tool with fully documented parameters and an output schema, the description covers the essential behavior and result. It does not mention setup requirements such as authentication, but the presence of a configure_auth sibling and the overall context make this a minor gap rather than a blocking one.

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%: all three parameters already have meaningful descriptions, enums, defaults, and an example for prompt. The description adds no parameter-specific detail, but it does not need to because the schema carries the full semantic load.

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 states a specific verb and resource: 'Create a new image from a text prompt', which clearly differentiates this from editing-oriented siblings like edit_image and continue_editing. It does not explicitly name those alternatives, so it stops short of a 5, but the purpose is unambiguous.

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 usage context is implied: this is for generating a brand-new image from a text prompt, which distinguishes it from image-editing tools. However, there is no explicit guidance about when not to use it or which sibling to choose for editing or continuation tasks.

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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