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Import character art as a sprite

import_character

Converts a character image into a Scratch sprite: removes the background, trims margins, scales it to fit the stage, and optionally creates talking costumes.

Instructions

Turns a jpg/png character picture into a sprite: cuts away the flat background so it sits properly on the backdrop, trims empty margins, scales it to fit the stage, and optionally builds talk frames — extra costumes that, alternated in a loop, make a still character look like it is talking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xNo
yNo
fileYesPath to a jpg or png
nameYesSprite name
sizeNoSprite size in percent, default 100
featherNoSoften the cut edge by this many pixels, default 1
toleranceNoHow far a pixel may differ from the corner colour and still count as background, default 42
max_pixelsNoLongest side in pixels after scaling, default 360
talk_framesNoAlso build -talk-a and -talk-b costumes, default true
remove_backgroundNoCut the flat background away, default true

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden, and it discloses the key behaviors: background removal, margin trimming, scaling, and optional talk-frame generation. It does not mention failure modes, project prerequisites, or whether an existing sprite is overwritten, but the main transformation behavior is clearly surfaced.

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 a single, front-loaded sentence that starts with the core action and follows with relevant processing details. Every clause informs the agent's mental model, and the talk-frame explanation is concise and useful rather than filler.

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 10 parameters, no annotations, and no output schema, the description covers the main transformation pipeline well but leaves x and y entirely unexplained, and they are also among the parameters without schema descriptions. The absence of any usage context, prerequisites, or return-value expectations leaves a noticeable gap for correct invocation.

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

Parameters4/5

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

Schema coverage is 80%, so baseline is 3, but the description adds genuine meaning by explaining why parameters like remove_background, feather, tolerance, max_pixels, and talk_frames exist. It connects these parameters to the underlying processing pipeline, going beyond raw schema descriptions.

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,

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 by focusing on turning a jpg/png character picture into a sprite, but it never explicitly says when to prefer this over siblings like add_sprite or add_costume, nor does it provide when-not-to-use guidance. The intended use case is inferable but not stated directly.

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