universal-image-mcp

transform_image

Apply a text prompt to transform an existing image—change style, add elements, or adjust colors—and save the edited result.

Instructions

Transform an existing image based on a text prompt.

Args: image_path: Path to the source image to transform. Supports common formats (PNG, JPEG, etc.) prompt: Text description of the desired transformation. Examples: "Make it black and white", "Add a rainbow in the sky", "Convert to watercolor painting style" model_id: Model identifier from list_models(). Examples: "amazon.nova-canvas-v1:0", "gpt-image-1.5", "models/gemini-2.5-flash-image" output_path: File path where the transformed image will be saved. Parent directories are created automatically.

Returns: Success message with output path, or error description.

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": {
      "image_path": "<image_path>",
      "prompt": "<prompt>",
      "model_id": "<model_id>",
      "output_path": "<output_path>"
    },
    "name": "transform_image"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes
`model_id`	Yes
`image_path`	Yes
`output_path`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.3Jul 31, 2026

TDQS

A4.6/5.0

Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses that the output is saved to output_path, that parent directories are created automatically, and that common image formats are supported. It does not explicitly address overwrite behavior or original-file preservation, but the explicit output_path sufficiently implies a new file is written.

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 one-sentence purpose, a clear Args list, and a Returns line. Every sentence contributes meaning; there is no fluff or repetition. It is appropriately sized for a 4-parameter tool.

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

Completeness5/5

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

Despite having no annotations and an unrevealed output schema, the description is self-sufficient: it explains what the tool does, every parameter, the expected output, and even auxiliary details like automatic directory creation and model sourcing. This allows an agent to invoke it correctly without external knowledge.

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?

Schema coverage is 0%, and the description compensates fully. Each parameter is explained with semantics: image_path includes format support, prompt includes multiple examples, model_id lists valid examples and references list_models(), output_path clarifies directory creation. This adds far more than the bare schema property names.

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 function: 'Transform an existing image based on a text prompt.' This specific verb-resource pair distinguishes it from sibling generate_image (which creates new images). The phrase 'existing image' is an explicit scope marker.

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 gives clear context: it is used to modify existing images with a text prompt. It also references list_models() for model selection, guiding users on how to obtain valid model IDs. However, it does not explicitly name alternatives or state when not to use this tool, only implying the contrast with generation.

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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