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edit_image

Modify one to four local PNG, JPEG, or WebP images by submitting a prompt with desired changes and elements to preserve. AI generation edits the image while keeping unspecified content intact.

Instructions

Edita imagens locais com $imagegen, preservando o que o prompt não mandar alterar.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoauto
promptYesAlterações desejadas e partes a preservar
qualityNomedium
mask_pathNoImagem opcional que marca a área a editar; normalmente omita
backgroundNoauto
image_pathsYesDe uma a quatro imagens PNG/JPEG/WebP locais
output_formatNopng
include_inlineNo
output_filenameNo
output_directoryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare the operation is not read-only and not idempotent, so the safety profile is covered. The description adds value by disclosing the preservation semantics and the $imagegen dependency, but it doesn't disclose output file behavior, whether inputs get overwritten, or any other side effects of the edit.

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?

A single sentence with no filler words, front-loading the verb and the core behavioral guarantee. It is lean and well-ordered, though it is arguably too terse for a 10-parameter tool.

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?

For a mutating, closed-world tool with 10 parameters, no output schema, and only 30% schema description coverage, a one-sentence description is under-specified. It omits output handling, mask-based editing semantics, image count limits, and what happens to the files after editing.

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 only 30%, so 7 of 10 parameters (size, quality, background, output_format, include_inline, output_filename, output_directory) lack schema documentation. The description does not compensate: it mentions editing, preservation, and local images, but never explains the mask, output, or formatting parameters.

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 states a specific verb and resource ('edits local images') and adds a distinctive behavioral clause: it preserves anything the prompt doesn't ask to change. This clearly differentiates it from the sibling generate_image, which creates new images rather than editing existing ones.

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

Usage Guidelines3/5

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

The description implies the usage context — modify existing local images while keeping unspecified content intact — but it never explicitly names alternatives or exclusion conditions. An agent must infer that generate_image is the right choice for new image creation; no when-to-use vs. when-not-to-use guidance is given.

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