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analyze_colors

Extract dominant colors from an image or region using K-Means clustering in LAB space. Get colors ranked by frequency with human-readable names for design and analysis.

Instructions

Extract dominant colors from an image region using K-Means clustering in LAB color space. Returns colors sorted by frequency with human-readable names from color.pizza.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNoNumber of dominant colors to return (default: 5)
bboxNoOptional bounding box as [ymin, xmin, ymax, xmax] normalized 0-1000. Defaults to full image.
imageYesPath to the image file or URL (http/https)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
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 does a fair job: it discloses the algorithm (K-Means), the color space (LAB), the ordering guarantee (sorted by frequency), and the naming source (color.pizza). It omits operational traits like cost/latency or behavior on unreadable images, but adds real value beyond what annotations would give.

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 tight sentences, zero filler, with the primary purpose and return contract front-loaded.

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?

No output schema exists, but the description compensates by explaining the return ordering and naming. Coverage of failure modes (invalid path, fetch errors) is missing, which is the main residual gap.

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%, so all three parameters (image, top, bbox) are already documented in the schema. The description adds nothing parameter-specific, so the baseline of 3 applies.

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 precise verb+resource ('Extract dominant colors from an image region') and even names the method (K-Means in LAB). It clearly reads as distinct from 'detect' and 'describe' siblings, though it never names or contrasts them explicitly.

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

Usage Guidelines2/5

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

No when-to-use or when-not-to-use guidance, and no mention of the alternatives 'describe_region' or 'detect' that an agent would need to choose between. The only implied usage comes from the schema's default notes.

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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