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jdsalasca

Aseprite Asset MCP

by jdsalasca

apply_dither_pattern

Fill a rectangular region with a uniform Bayer-dithered blend of two colors, creating a textured mix for pixel art.

Instructions

Fill a rectangle with a uniform Bayer-dithered mix of two colors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xYes
yYes
widthYes
heightYes
color_aYes
color_bYes
densityNo
filenameYes
layer_nameYes
frame_indexYes
create_if_missingNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It says 'Fill' which implies a mutating operation, but it doesn't disclose whether it creates the layer if missing (though create_if_missing param hints at it), whether it overwrites existing pixels, how density affects the pattern, or what happens with out-of-bounds coordinates. The description adds minimal behavioral context beyond the operation itself.

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?

One sentence, no waste, and the key concepts (rectangle, uniform Bayer dither, two colors) are front-loaded. It's appropriately concise for a simple operation, though it could add a bit more detail without becoming bloated.

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 an 11-parameter tool with no annotations and no output schema, the description is too thin. It doesn't explain the density parameter's role, the create_if_missing behavior, or how this relates to the layer/frame system. An agent would need to inspect the schema and guess at semantics for most parameters.

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 0%, so the description must compensate. It mentions 'two colors' (color_a, color_b) and 'rectangle' (x, y, width, height), but doesn't explain density, create_if_missing, frame_index, or layer_name semantics. The description adds some meaning for 2 of 11 parameters but leaves the rest undocumented.

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 states a specific verb ('Fill'), a resource ('a rectangle'), and the method ('uniform Bayer-dithered mix of two colors'). It clearly distinguishes from the sibling 'apply_dither_gradient' by specifying 'uniform' and 'two colors'. However, it doesn't explicitly name the sibling or contrast with it, so it's clear but not fully differentiated.

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 usage: when you need a dithered fill of a rectangle with two colors. It doesn't explicitly state when to use this vs. alternatives like apply_dither_gradient, draw_rectangle, or fill_area. The context is clear enough for an agent to infer, but no explicit when/when-not guidance is given.

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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