Colour Memory

Ecommerce Product Copy from Archive Colour

ecommerce_copy

Read-only

Generate complete ecommerce product copy for any colour. Input: hex + product type + tone + channel. Output: colour name, product title, short description, long description, SEO title, meta description, alt text, Instagram caption, and cross-sell suggestion. Every piece of copy is grounded in archive provenance -- never generic AI colour copy. The colour name comes from the nearest archive match, not invented. Examples: velvet cushion in Murex Luxury, ceramic vase in Woad Vat Blue, linen throw in Standlake Silt. Directly useful for Shopify, WooCommerce, and editorial product pages.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`hex`	Yes	Hex colour of the product e.g. '#4A2A50'
`tone`	No	Copy tone e.g. 'premium but not pompous', 'warm and accessible', 'heritage and serious'	premium but not pompous
`channel`	No	Sales channel e.g. 'shopify', 'etsy', 'instagram', 'editorial'	shopify
`brand_name`	No	Optional brand name to include in copy
`product_type`	Yes	Product type e.g. 'velvet cushion', 'ceramic vase', 'linen throw', 'candle'

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior4/5

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

Annotations provide readOnlyHint=true, and the description adds meaningful behavioral context: every piece of copy is grounded in archive provenance, the colour name comes from the nearest archive match (not invented), and output is never generic AI copy. This goes beyond the annotation by setting expectations about grounding and provenance behavior.

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 dense but every sentence contributes: purpose, input/output summary, provenance guarantee, examples, and target use cases. It is front-loaded with the main action and structured logically, making it easy to scan.

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?

The description fully explains what the tool does, the output items, key behavioral traits (archive provenance), examples, and relevant channels. Since an output schema exists, return values need not be detailed. Given the tool's complexity, this is complete.

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?

The input schema covers 100% of parameters with descriptions, so baseline is 3. The description adds value through examples that illustrate parameter combinations (e.g., 'velvet cushion in Murex Luxury') and clarifies the purpose of hex and product_type, but does not deeply explain each parameter beyond schema coverage.

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 generates complete ecommerce product copy for any colour, with specific outputs listed. It distinguishes itself from siblings like ecommerce_namer and palette tools by focusing on full product copy grounded in archive provenance, not just naming or palette analysis.

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 clearly indicates the tool is for ecommerce product copy generation and mentions channels like Shopify, WooCommerce, and editorial. It does not explicitly name alternatives or when-not-to-use, but the context is clear enough for an agent to select it appropriately.

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

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