Gemini Gen MCP

text_to_image

Generate images from text descriptions with customizable aspect ratios and generation parameters using Gemini image models.

Instructions

Generate images from text using Gemini's Flash (Nano Banana) Image models.

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": "text_to_image"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes	Text description of the image to generate
`model`	No		gemini-2.5-flash-image
`aspect_ratio`	No		1:1
`temperature`	No	Sampling temperature for image generation (default: 1.0)
`top_p`	No	Nucleus sampling parameter for image generation (optional)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.0.4May 31, 2026

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are present, so the description must carry the full burden of behavioral disclosure. It only states that images are generated, but fails to mention whether generation is synchronous, any auth requirements, cost implications, or rate limits. This is minimal for a generative tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no wasted words. It is efficient but could be slightly expanded without losing conciseness to improve completeness.

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

Completeness2/5

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

Given 5 parameters, no output schema, no annotations, and one sibling, the description is insufficient. It omits return format, model differences, prompt best practices, and potential error states, leaving agents underinformed for correct invocation.

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

Parameters2/5

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

The description does not add any meaning to the parameters beyond what is already in the input schema. With 60% schema coverage, it misses the opportunity to clarify parameter usage, defaults, or relationships.

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 explicitly states the verb 'generate', resource 'images', input 'text', and specifies the model 'Gemini's Flash (Nano Banana) Image models', clearly distinguishing it from the sibling tool 'text_to_speech' which generates speech.

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?

No explicit when-to-use or when-not-to-use guidance is provided. While the purpose is distinct from the only sibling, the description does not offer any context on ideal scenarios or limitations, leaving usage entirely implied.

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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