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Fix a palette to be colorblind-safe

fixPalette

Adjusts color palettes that fail colorblind-safety checks, returning minimally altered colors separated in lightness so they pass.

Instructions

Take a set of colors that fails the colorblind-safety check and return an adjusted set that passes, staying as close to the originals as possible. Conflicting colors are separated in lightness (the axis color-vision deficiency preserves). Returns the new colors, how far each moved, and whether it fully passes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
colorsYesHex colors to fix, e.g. ["#d7191c","#1a9641"]

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
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 reasonably well: it discloses the optimization objective ('staying as close to the originals as possible'), the mechanism ('separated in lightness'), the rationale for that axis, and the three-part return payload. It stops short of stating behavior for already-passing inputs, single-color inputs, or whether output is deterministic.

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?

Three sentences, front-loaded with the transformation and its guarantee, then the mechanism, then the return description. No filler and no repetition of the title.

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?

There is no output schema, so the description correctly compensates by enumerating what comes back (new colors, per-color movement, pass/fail status). Combined with the stated optimization goal and axis of separation, an agent has enough to call and interpret the tool; only edge-case behavior is unaddressed.

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?

Only one parameter and schema description coverage is 100%, so the schema already documents 'colors' with a hex example. The description adds no additional format or constraint detail (e.g., accepted color notations, array length limits), which matches the baseline of 3 when the schema does the heavy lifting.

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?

Specific verb (fix/adjust) applied to a specific resource (a color set that fails colorblind-safety), and the description implicitly separates it from siblings like checkPalette (diagnose only) and generateSafePalette (create from scratch). The phrase 'take a set of colors that fails... and return an adjusted set' makes the input contract and transformation unmistakable.

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 a precondition (input must be a failing set), which gives some usage context, but it never names an alternative or states when to prefer fixPalette over generateSafePalette or checkPalette. Usage must be inferred from the sibling names rather than the text.

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