Colour Memory

Style Match — Does This Go With That?

style_match

Read-only

The colour question every stylist gets asked: does this bag go with this outfit? Submit your outfit items as hex values with labels (dress, bag, shoes, coat, belt, scarf, etc.) and receive a verdict on what works, what clashes, what is missing, and what to add. Every recommendation is backed by archive colour names and historical context — not generic colour theory, but documented cultural combinations. Also suggests one missing archive colour that would complete the look. Examples: 'I have a navy dress (#1C3A6E) and a tan bag (#C8A87A) — what shoes?' or 'Does this burgundy coat work with olive trousers?' 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
`ask`	No	Optional: specific question e.g. 'what bag colour works?' or 'do the shoes work?'
`items`	Yes	List of outfit items with label and hex colour
`occasion`	No	Optional: occasion context e.g. 'daytime', 'evening', 'office', 'casual', 'wedding guest'	general

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses important behavior: it returns a verdict with recommendations, includes a suggested missing archive colour, and 'result already carries the rendered palette and its PNG, PDF, ASE, JSON and CSS downloads.' It also warns against presenting derived archive anchors as recommendations and instructs calling palette_finalize if the agent chooses its own palette. This is rich, annotation-complementing transparency.

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 longer than necessary but each part serves a purpose: usage context, examples, output behavior, and a safety instruction about archive anchors. Its structure is front-loaded with the key use case followed by actionable details. The rhetorical opener adds tone but still conveys the tool's domain.

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?

Given the output schema exists, the description does not need to detail return values; it nonetheless announces the rendered palette/downloads and tells the agent what to show the customer. It covers input format, example questions, occasion context through the schema, and adds cross-tool routing to palette_finalize. No critical guidance appears missing.

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?

Schema description coverage is 100%, so baseline is 3. The description adds concrete item semantic meaning—'hex values with labels (dress, bag, shoes...)', and gives example user phrasings for the ask parameter. It does not elaborate on occasion, but the schema already defines it and covers the default; still, the item/ask examples push this above baseline.

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 identifies the tool as a style-matching service: submit outfit items with hex values and labels, and receive a verdict on what works, clashes, or is missing. This is a specific verb+resource (matching outfit colours), and the phrase 'Style Match' plus the outfit-item examples distinguish it from sibling tools like palette_verdict or colour_combination without requiring schema inspection.

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 establishes when to use it through the stylist-question framing and concrete examples, giving clear context for outfit-matching queries. It does not explicitly name alternatives or exclusions, but the examples and item-label scope make the intended use obvious.

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

A3.6/5.0

Disambiguation2/5

Several tool clusters perform near-identical functions: extract_image_colours, image_palette and palette_extract all extract dominant colours from images; colour_passport, colour_dna, colour_metrics and colour_cultural_risk all profile a single hex; and at least six compound 'complete package' tools (agent_brief, archive_report_brief, brand_report, design_session, image_brief, session_brief) overlap heavily in scope. The descriptions try to differentiate -- some even point to tool_guide for routing -- but the volume and similarity of clusters makes misselection likely.

Naming Consistency4/5

The vast majority of tools follow a clear domain_prefix_suffix pattern (colour_, palette_, archive_, brand_, accessibility_, project_) and within families naming is very disciplined (brand_guideline_specify/select/pdf/claims/status, project_get/list/versions/delete). However, a handful of outliers invert the order (extract_image_colours, ingest_image, render_colour_result, query_hex) and some descriptions reference tools that don't exist as endpoints (palette_from_concept, match_paint_system, get_colour_metrics).

Tool Count1/5

88 tools is far beyond any reasonable single-server surface, even for a platform spanning archives, branding, interiors and accessibility. The sheer number forces agents into a massive decision space, and many tools exist purely as convenience wrappers that replace chains of 3-6 other tools, suggesting aggressive consolidation was needed.

Completeness4/5

The surface covers an unusually broad domain -- archive search, colour science, palettes, branding, interiors, accessibility, image extraction, projects, and PDF/Word/Excel exports -- with very few dead ends for end-user workflows. Minor gaps: several compound-tool descriptions reference tools that no longer exist, and valid archive names are only discoverable via error messages rather than a dedicated listing endpoint.

Resources

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