Colour Memory

by app.railway.up.colour-memory-api-production

Palette Quality Audit

palette_audit

Read-only

Full palette quality audit. Scores on accessibility, cultural risk, tonal balance, colour diversity, and archive naming strength. Returns overall score 0-100, grade, and prioritised fix list. Enterprise quality gate -- use before shipping any palette. Deterministic, no LLM cost. The result already carries the rendered palette and its PNG, PDF, ASE, JSON and CSS downloads -- show them to the customer. Never present the archive anchors a colour was derived from as the colours you are recommending. If you go on to choose a final palette OF YOUR OWN from this evidence, call palette_finalize once with those exact colours so the customer can see and download what you actually recommended.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`market`	No	Target market	global
`medium`	No	digital \| print \| both	digital
`palette`	Yes	Hex values to audit
`use_case`	No	Use case context	brand identity

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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

Annotations declare readOnlyHint=true, and the description adds meaningful behavioral context: deterministic execution, no LLM cost, result carrying rendered palette and downloadable PNG/PDF/ASE/JSON/CSS, and the archive-anchor presentation caveat. No contradiction with read-only semantics.

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?

The purpose is front-loaded and every sentence earns its place: scope, outputs, quality-gate usage, bundled deliverables, the archive-anchor warning, and the palette_finalize follow-up. There is no filler or redundant restatement of the title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and all parameters documented in the input schema, the description supplies the missing context: expected outputs, customer-facing downloads, deterministic behavior, and the post-audit workflow. An agent has everything needed to select and invoke this tool correctly.

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 the baseline is 3. The description adds no parameter-level detail beyond the schema and does not explain how market, medium, or use_case affect the audit, but the schema already documents each parameter.

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?

States the exact verb+resource: 'Full palette quality audit' with explicit audited dimensions (accessibility, cultural risk, tonal balance, colour diversity, archive naming strength) and concrete outputs (0-100 score, grade, prioritised fix list). This clearly distinguishes it from sibling tools like palette_analyse or palette_verdict, reinforced by 'Enterprise quality gate'.

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

Usage Guidelines5/5

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

Gives an explicit trigger: 'use before shipping any palette.' It also provides a conditional follow-up instruction to call palette_finalize when the agent chooses its own final palette, plus a critical negative guard: never present archive anchors as recommended colours. This is strong when-to-use and when-not-to guidance.

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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TDQS

A3.7/5.0

Disambiguation2/5

Multiple tool clusters have unclear boundaries: colour_passport, colour_dna, colour_metrics, and colour_cultural_risk overlap heavily; palette_extract, extract_image_colours, image_palette, and ingest_image cover similar image-colour extraction territory; and compound tools like design_session, image_brief, session_brief, and archive_report_brief duplicate chains of component tools. The descriptions are detailed and occasionally state 'use this instead of X', but an agent still faces many near-duplicate choices.

Naming Consistency3/5

Names are consistently snake_case and often use domain prefixes (colour_, palette_, archive_, brand_, accessibility_), but verb placement is mixed: some are verb_noun (extract_image_colours, query_hex, style_match), others are noun_verb (colour_dna, palette_generate, brand_audit), and a few standalone names (ui_states, tool_guide, meta_capabilities) don't fit either pattern. The convention is readable but not uniform.

Tool Count1/5

At 88 tools, this is far beyond the reasonable well-scoped range and exceeds the 50+ extreme mismatch threshold. Many tools are compound wrappers that consolidate chains of simpler tools, adding redundancy and cognitive load rather than genuine coverage.

Completeness5/5

For the apparent domain, the tool surface is extremely comprehensive: colour extraction, analysis, naming, accessibility, cultural/provenance research, palette generation, brand systems, interior design, ecommerce copy, image briefs, project lifecycle, exports, and diagnostic tools are all present. Persistent objects have list/get/versions/delete/export support, so there are no obvious dead ends.

Resources

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