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kevinten-ai

Gemini Image Gen MCP Server

by kevinten-ai

generate_image

Generate an image from a text prompt using Google Gemini or Imagen models. Supports multiple models; retry with a different model if quota limit is reached.

Instructions

Generate an image from a text prompt using Google Gemini or Imagen. Provider: ai-studio. Default model: gemini-3.1-flash-image. Available models: gemini-3.1-flash-image, gemini-3-pro-image, gemini-2.5-flash-image. Tip: if you hit a 429 quota error, retry with a different model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel to use (optional, default: gemini-3.1-flash-image). Available: gemini-3.1-flash-image, gemini-3-pro-image, gemini-2.5-flash-image
promptYesThe text prompt for image generation
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses the provider, default model, and available models, and a tip on handling quota errors. However, it lacks details on whether the tool has side effects (e.g., state changes), authentication requirements, rate limits, or output format (e.g., URL vs base64). The tip about quota is useful but not comprehensive.

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 sentences: first states purpose, second provides provider and default, third lists models and tip. Efficient and front-loaded with no redundant information.

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?

Despite good input schema coverage, the tool lacks an output schema and the description does not explain what the tool returns (e.g., image URL, binary, or file). Also missing info about safety filters, content policies, aspect ratio, or any other generation parameters beyond prompt and model. This leaves agents guessing the response format.

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?

Schema coverage is 100% with both parameters described. The description adds value by listing available models explicitly and providing a tip about retrying on quota errors, which helps agents choose model parameter wisely. This goes beyond the schema's enum list.

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 verb "generate" and the resource "image from a text prompt" with specific provider (ai-studio) and default model. It distinguishes itself well even without sibling tools.

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?

Provides clear context that it uses Google Gemini or Imagen, and offers a practical tip for quota errors by retrying with different models. However, it does not explicitly state when not to use this tool or provide alternative scenarios.

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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