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Colour Memory

by io.github.DigbyO

Product Line Colour Namer

ecommerce_namer
Read-only

Generate archive-grounded colour names for up to 40 product SKUs. Input: list of hex values, product category, brand name, naming style. Output: for each hex -- archive name, source citation, one-line product description, dE2000 match distance, match quality, and confidence score. Every name is archive-sourced, not invented. Each carries a primary source citation that can be defended to buyers, press, and brand teams. Use for paint ranges, candle collections, fashion lines, homeware, cosmetics. Style options: geographical, poetic, material, literary, mixed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hexesYesList of hex values e.g. ['#D4A829', '#1A5C6E']
styleNogeographical | poetic | material | literary | mixed (default)
max_dENoMax dE2000 distance to accept (default 25)
brand_nameNoBrand name for context
product_categoryNoe.g. 'paint', 'candle', 'fashion', 'homeware'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, and the description does not contradict this. It adds valuable behavioral context: 'Every name is archive-sourced, not invented' and 'Each carries a primary source citation that can be defended to buyers, press, and brand teams', which clarifies the guarantee of traceability.

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 description is well-organized with a clear input/output structure, and every sentence contributes meaningful information: scope, use cases, guarantees, and options. It is detailed yet efficient, avoiding redundancy with the schema by specifying unique attributes like 'up to 40 SKUs' and 'dE2000 match distance'.

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 full schema coverage, the description need not repeat return structures, but it enriches understanding by explaining the output semantics (source citation, dE2000, confidence). It covers input, output, use cases, and constraints, making the tool fully comprehensible to an 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 description coverage is 100%, so the schema already documents all five parameters. The description mentions the general input types (hex values, category, brand, style) but does not add new details beyond what the schema provides, keeping this at the 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 opens with a specific verb phrase 'Generate archive-grounded colour names for up to 40 product SKUs', clearly defining both the action and resource. It also distinguishes itself from sibling tools like colour_namer by emphasizing 'archive-grounded' and 'product SKUs'.

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 provides clear use contexts: 'Use for paint ranges, candle collections, fashion lines, homeware, cosmetics.' However, it does not explicitly name alternatives or state when not to use this tool, though the archive-grounded nature implies differentiation.

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

A3.8/5.0
Disambiguation2/5

With 88 tools, there is substantial overlap: colour_passport vs colour_dna vs colour_metrics vs colour_cultural_risk are explicit components of the same object; palette_concept vs palette_strict vs palette_generate vs palette_heritage overlap heavily; and four image extraction tools exist (extract_image_colours, image_palette, palette_extact, ingest_image). Although descriptions are detailed and tool_guide exists, an agent will frequently struggle to select the correct tool unambiguously.

Naming Consistency5/5

Nearly all tools follow a consistent snake_case noun_verb or domain-prefixed pattern (colour_*, palette_*, brand_*, archive_*, project_*, accessibility_*). The naming is uniform and predictable, with no mixing of styles or verb conventions across the set.

Tool Count1/5

88 tools is an extreme count for an MCP server. Even honoring the broad domain, the rubric places 50+ at the extreme end, and the high overlap between compound and individual tools suggests many could be consolidated or exposed as sub-resources rather than top-level tools.

Completeness5/5

The tool surface covers the full colour lifecycle: lookup, analysis, palettes, brand systems, accessibility, image extraction, interior design, archival research, reports, PDF generation, and project management. Workflows have clear entry points and few dead ends, and the presence of compound tools further closes integration gaps.

Resources