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gimp_remove_background

Removes image backgrounds by running AI segmentation on a layer and converting the result into an editable mask or applying it directly to the alpha channel.

Instructions

AI subject cut-out. Runs a segmentation model on the layer and writes the result as an editable layer mask (mode='mask') or bakes it into the alpha channel (mode='apply'). Needs the optional segmentation extra. Models: u2net (default), isnet-general-use, u2net_human_seg, isnet-anime, silueta.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNomask
modelNou2net
layer_idYes
alpha_mattingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the burden of disclosure. It does disclose the main behaviors: writing a layer mask or baking into the alpha channel, and the dependency on the segmentation extra. However, it does not mention whether the operation overwrites an existing mask/alpha channel or any caveats about the 'apply' mode being destructive.

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 compact, front-loaded with the core purpose, and every sentence adds useful information: function, modes, prerequisite, and model options. No redundant wording.

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?

Given the output schema exists and the tool is relatively focused, the description covers the essential invocation context: required extra, model choices, and mode semantics. The main gap is the undocumented 'alpha_matting' parameter and lack of detail about existing mask/alpha overwrite behavior, but overall it is nearly complete.

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

Parameters3/5

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

The description adds meaning for 'mode' (mask vs apply) and 'model' (listing valid values and defaults), compensating for the 0% schema coverage. However, 'alpha_matting' is not explained at all, leaving a meaningful parameter undocumented.

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 action ('AI subject cut-out', 'runs a segmentation model on the layer') and clearly distinguishes this from sibling tools like gimp_layer_mask or gimp_apply_filter. It also differentiates the two output modes, making the tool's purpose unmistakable.

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 for when to use this tool: when AI-based subject/background removal is needed, and notes the required optional segmentation extra. It does not explicitly name alternatives or exclusions, 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.

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