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genart-mcp

by genart-dev

design_trace_regions

Segments an image into color regions using k-means clustering and returns dominant colors and region masks, optionally creating a trace layer for design work.

Instructions

Segment the image into color regions using k-means clustering. Returns dominant colors and region masks.

Input Schema

NameRequiredDescriptionDefault
kNoNumber of color clusters (default: 5).
sourceIdNoReference image asset ID (default: current canvas).
createLayerNoCreate a trace layer (default: true).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

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 helpfully discloses the return payload ('dominant colors and region masks') and the algorithm, but omits that createLayer defaults to true, meaning the tool mutates the document by adding a layer — a side effect an agent should know about, plus nothing on cost or undo.

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 short sentences, purpose first and return value second, with no filler or repetition. Every clause carries information.

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 3-optional-parameter tool with a full schema and no output schema, the description covers what it does and what it returns. It is missing the document-mutating effect of the default createLayer behavior and any guidance on choosing it over sibling trace tools, which leaves real gaps given no annotations.

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?

Schema description coverage is 100%, with k, sourceId, and createLayer all documented at the property level including defaults, so the baseline is 3. The description adds no parameter-specific meaning (e.g., what a higher k does to output size or cost), so it neither compensates nor detracts.

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?

States a specific verb and resource ('Segment the image into color regions') plus the method (k-means), which distinguishes it in spirit from the many other design_trace_* siblings that trace edges, lines, contours, or values. It never names those siblings explicitly, so discrimination relies on the reader inferring 'color regions' vs 'edges/lines'.

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 phrase 'color regions using k-means clustering' implies the situation (reducing a raster into flat color areas), but there is no explicit when-to-use, when-not-to-use, or named alternative among the trace family. An agent must infer its niche relative to design_trace_edges, design_trace_values, and design_trace_shapes.

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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