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Claude Gemini Bridge

Claude Gemini Bridge

by archolet
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

generate_image

Generate design assets from text prompts using Gemini or Imagen models. Specify aspect ratio, resolution, and output format for custom images.

Instructions

Generate design assets using Gemini or Imagen models.

Args: prompt: Description of the asset. model: Model name. aspect_ratio: Image ratio. output_format: base64 or file. output_dir: Save directory. number_of_images: Image count. output_resolution: Resolution (1K/2K).

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

NameRequiredDescriptionDefault
modelNogemini-3-pro-image-preview
promptYes
output_dirNo./images
aspect_ratioNo1:1
output_formatNobase64
number_of_imagesNo
output_resolutionNo1K

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must fully disclose behavior. It mentions model names but omits critical traits like authentication, cost implications, rate limits, or whether generation is synchronous. The agent lacks insight into side effects or prerequisites.

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 concise, with the main purpose front-loaded and an organized Args list. However, the parameter descriptions are terse, sacrificing depth for brevity. The structure is efficient but could benefit from additional context.

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 7 parameters, no output schema, and no annotations, the description is incomplete. It does not explain return format, side effects, or constraints like image limits. The agent cannot fully anticipate tool behavior, especially regarding output handling for base64 vs file.

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?

Schema description coverage is 0%, so the description must compensate. However, parameter explanations are minimal (e.g., 'Image ratio' for aspect_ratio), offering little beyond schema titles. For example, 'model' lacks valid values, and 'output_format' doesn't clarify outcomes (base64 vs file path). This fails to resolve ambiguity for the agent.

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 'design assets', implying image generation. It distinguishes from sibling tools like design_from_reference by specifying use of Gemini or Imagen models, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like design_from_reference or refine_frontend. It lacks context for selection, such as scenarios where image generation is appropriate vs. other design 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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