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miracorhan

Open Google Image Generator MCP

by miracorhan

tool_transform_image

Transform an image with a text prompt via Gemini models for style transfer, scene rewriting, or natural-language edits that do not need pixel-precise masking.

Instructions

Free-form 'image + text -> image' transformation via Gemini multimodal models. Use for style transfer, scene rewriting, or any natural-language image edit that does not require pixel-precise masking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.3.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the modality contract and that a third-party multimodal model performs the work (implying a network call), but says nothing about file side effects, cost/latency, auth, or rate limits — all relevant for a tool whose schema includes output_path and return_base64.

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?

Two tight sentences: the first states what it does and how, the second states when to use it and when not to. Zero filler and front-loaded.

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

Completeness3/5

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

For a tool with nine schema parameters, no annotations, and no output schema, the description covers the core routing decision but omits the multi-reference-image capability, tier selection, and whether a file is written to disk versus returned inline. Adequate for selection, thin 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?

Schema description coverage is reported at 0% for the top level, and the description never mentions any parameter — no guidance on model_name/model_tier selection, save_format, additional reference images, or output destinations. The nested property descriptions carry the load, but the description itself adds no parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete verb and resource ('image + text -> image' transformation) and even specifies the underlying mechanism (Gemini multimodal models). It also draws a capability boundary against masking-based edits, which distinguishes it from sibling tool_edit_image, though it never names that sibling explicitly.

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?

It gives explicit use cases (style transfer, scene rewriting, natural-language edits) and a negative condition ('does not require pixel-precise masking'), which routes the agent away from mask-based tools. The alternative is only implied rather than named, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.