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Iterate and Refine a Palette

palette_iterate

Submit a palette and natural language feedback to get a refined palette with change rationale, grounded in archival data.

Instructions

Refine an existing palette using natural language feedback. Submit your current palette and feedback such as more melancholic, too corporate add warmth, or better for Gen Z luxury. Returns a refined palette with archive grounding and change rationale.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paletteYesCurrent hex palette to refine
feedbackYesNatural language refinement e.g. more melancholic
directionNoAlias for feedback — natural language direction e.g. more dangerous, more historical, warmer
use_caseNoUse case context e.g. luxury homewares
marketsNoTarget markets
n_resultsNoNumber of variants to return (default 1)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
resultNo
errorNo
Behavior4/5

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

The description specifies the return includes 'archive grounding and change rationale,' adding behavioral detail beyond the readOnlyHint annotation. However, it does not disclose whether the original palette is modified or preserved, which is a minor gap.

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 efficient sentences with immediate verb 'Refine.' No wasted words, front-loaded with purpose and input/output structure.

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?

For a 6-param tool with output schema, the description covers core functionality and output. It omits potential error conditions and the meaning of 'archive grounding,' but overall it is adequate for an AI agent.

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 coverage is 100% with descriptions for all parameters, so the description adds limited extra meaning. Examples like 'more melancholic' are illustrative but not necessary beyond the schema.

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 clearly states the tool refines an existing palette using natural language feedback, distinguishing it from palette_generate (creation) and palette_audit (evaluation). The verb 'refine' and resource 'palette' are specific and unambiguous.

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

Usage Guidelines4/5

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

The description implies usage for modifying an existing palette but does not explicitly name alternatives or state when not to use it. The context of sibling tools provides differentiation, but explicit guidance is missing.

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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