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Gemini Image Generator MCP Server

Gemini Image Generator MCP Server

by qhdrl12
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

transform_image_from_encoded

Modify existing images using text prompts with Google's Gemini AI. Provide a base64-encoded image and describe desired changes to generate transformed versions.

Instructions

Transform an existing image based on the given text prompt using Google's Gemini model.

Args:
    encoded_image: Base64 encoded image data with header. Must be in format:
                "data:image/[format];base64,[data]"
                Where [format] can be: png, jpeg, jpg, gif, webp, etc.
    prompt: Text prompt describing the desired transformation or modifications
    
Returns:
    Path to the transformed image file saved on the server

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": {
      "encoded_image": "<encoded_image>",
      "prompt": "<prompt>"
    },
    "name": "transform_image_from_encoded"
  }
}

Input Schema

NameRequiredDescriptionDefault
encoded_imageYes
promptYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses the tool uses Google's Gemini model and that it saves the transformed image on the server, which are useful behavioral traits. However, it doesn't mention rate limits, authentication requirements, file size limits, or potential side effects of the transformation process.

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 efficiently structured with a clear opening sentence stating the purpose, followed by well-organized sections for Args and Returns. Every sentence earns its place by providing essential information without redundancy.

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 2-parameter tool with no annotations and no output schema, the description provides good coverage of purpose, parameters, and basic behavior. It explains what the tool does, how to format inputs, and what to expect as output. The main gap is lack of information about error conditions, performance characteristics, or more detailed behavioral constraints.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by providing detailed semantics for both parameters. It specifies the exact format required for encoded_image (including header format and supported image types) and explains what the prompt parameter should contain. This adds significant value beyond the bare schema.

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 tool's purpose with specific verb ('Transform') and resource ('an existing image'), and distinguishes it from siblings by specifying it uses encoded image data rather than text or file inputs. The mention of Google's Gemini model adds technical specificity.

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?

The description provides clear context about when to use this tool (transforming existing images with encoded data) and implicitly distinguishes it from siblings (generate_image_from_text for text-to-image, transform_image_from_file for file-based transformation). However, it doesn't explicitly state when NOT to use this tool or mention specific prerequisites.

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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