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record_taste_decision

Destructive

Capture design taste decisions as they happen, including chosen option, rejected alternatives, and rationale, to refine future taste interviews.

Instructions

The Taste Engine's learning loop — record a taste, direction, or design decision the MOMENT it is made during real work (an accent chosen, a nav pattern rejected, a name direction picked, a type pairing approved), not just at interview time. Each record carries the project, a short dimension name (a standard one like color/navigation or a new category like iconography/sound), what was chosen in the user's words, the alternatives rejected, why, and a source: 'user-directed' (the user asked for it), 'user-approved' (the user accepted a proposal), or 'user-corrected' (the user overrode a generated choice — the highest-signal record). Recorded decisions evolve every future get_taste_interview kickoff: recurring choices return as suggested defaults on their dimension's question, and decision categories no standard question covers become NEW interview questions. Record liberally — every committed decision is calibration data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whyNoThe stated reason, if the user gave one.
sourceNoHow the decision was made — defaults to 'user-directed'. 'user-corrected' (user overrode a generated choice) is the highest-signal record.
profileYesTaste profile name (see list_taste_profiles).
projectYesProject the decision was made on.
decisionYesWhat was chosen, in the user's words — e.g. 'amber-phosphor accent, period-accurate not decorative'.
rejectedNoAlternatives considered and passed over.
dimensionYesShort lowercase dimension name — a standard one (typography, spacing, color, layout, motion, imagery, entrance, loading, navigation, aesthetic, libraries) or a new category the standard set doesn't cover (iconography, sound, naming, …). New categories become new interview questions.
Behavior4/5

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

Annotations already indicate destructiveHint=true, so this is a write operation. The description adds context on how records affect future interviews and explains the signal strength of different source types. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and is fairly concise given the complexity. It could be slightly trimmed (e.g., the impact on interviews could be shorter), but every sentence adds value.

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, but for a write tool that's acceptable. The description thoroughly covers the purpose, parameters, and impact. Given 7 parameters and the tool's role in the system, it is adequately complete.

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?

The input schema has 100% coverage with descriptions, but the description adds extra meaning: dimension can be standard or new (and new ones become interview questions), source enum values are clarified, and 'user-corrected' is flagged as highest-signal. This goes beyond 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 uses specific verb 'record' and resource 'taste, direction, or design decision', clearly distinguishing it from siblings like decision_add or decision_commit. It explains exactly what constitutes a decision and when to record.

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?

Explicitly says to record 'the MOMENT it is made' and contrasts with 'not just at interview time'. It also explains the downstream effect on get_taste_interview kickoff, but does not explicitly compare to alternatives or state when not to use.

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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