Open Google Image Generator MCP

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tool_generate_image

Generate images from text prompts using Google Vertex AI Gemini models, with selectable model tiers and options for aspect ratio, output format, and file saving.

Instructions

Generate an image from a text prompt using Vertex AI Gemini image models (Nano Banana). Use model_tier for simple model selection: fast/balanced/quality/ultra. negative_prompt / seed / watermark / safety / storage_uri apply to legacy Imagen models only.

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": {
      "params": "<params>"
    },
    "name": "tool_generate_image"
  }
}

Input Schema

Name	Required	Description	Default
`params`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.3.0Oct 18, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It does disclose a non-obvious behavioral trait — which parameters only take effect on legacy Imagen models — but says nothing about side effects (file written to disk vs. base64 returned), cost, rate limits, or permissions. Partial coverage of a mutation-style tool.

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?

Three short lines, front-loaded with the core action, then the tier shortcut, then the legacy caveat. Every sentence carries distinct information with no filler.

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?

Covers the core action and model-selection path, but with no output schema and no annotations the agent gets no statement of what is produced (saved path vs. base64 payload) or how to choose this over sibling generation/edit tools. Adequate but leaves real gaps for a 16-parameter generation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The reported 0% coverage is an artifact of the single top-level $ref; the nested GenerateImageParams schema actually describes every field. The description still adds cross-parameter semantics the schema does not: model_tier overrides model_name, and the legacy-only grouping of negative_prompt/seed/watermark/safety/storage_uri (only some of which are marked Imagen-only in the schema itself).

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?

States a specific verb and resource ('Generate an image from a text prompt') plus the backing engine (Vertex AI Gemini image models / Nano Banana). This implicitly separates it from tool_edit_image and tool_transform_image, which operate on existing images, though it never names those siblings explicitly.

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?

Gives conditional guidance ('Use model_tier for simple model selection') and a useful applicability caveat that negative_prompt/seed/watermark/safety/storage_uri are legacy-Imagen-only. However, it never says when to prefer this tool over tool_batch_generate or tool_edit_image, so routing between siblings is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.