Colour Memory

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

Generate Archive-Grounded Colour Names

colour_namer

Read-only

Generate memorable, archive-verified colour names for any hex value. Choose from naming styles: geographical, poetic, material, literary, botanical, industrial, or mixed. Every name is grounded in a real archive source. The core of the Shopify product naming use case.

Input Schema

TableJSON Schema

Name	Required	Description
`hex`	Yes	Hex colour to name e.g. #8B4A2A
`style`	No	geographical \| poetic \| material \| literary \| botanical \| industrial \| mixed
`market`	No	Target market e.g. UK luxury
`n_names`	No	Number of name options (default 5)
`product_type`	No	Product type e.g. candle, paint, leather bag

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJun 20, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "description": "Structured JSON response. Shape varies by tool; most return an 'ok' boolean plus a 'result' or 'results' field with the tool's data payload.",
+  "type": "object"
+}

Changed1 schema field changedJun 17, 2026

changedOutput schema / (root)

Previous value: -{
-  "properties": {
-    "error": {
-      "type": "string"
-    },
-    "ok": {
-      "type": "boolean"
-    },
-    "result": {
-      "type": [
-        "object",
-        "array",
-        "string",
-        "null"
-      ]
-    }
-  },
-  "type": "object"
-}New value: +null

AddedJun 2, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations declare readOnlyHint=true, so the safe, read-only nature is already known. The description adds useful behavioral context beyond that: names are 'archive-verified' and 'grounded in a real archive source,' and it lists the naming styles. It does not contradict annotations, and it supplements them with meaningful details about the tool's 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 three concise, front-loaded sentences. The first sentence states the core action, the second lists options, and the third explains the unique value and primary use case. There is no filler or repetition; every sentence contributes.

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?

Given the tool's complexity (5 parameters, 1 required) and the presence of an output schema, the description is adequately complete. It covers purpose, style options, archive grounding, and a concrete use case. It could mention when to prefer alternative tools, but that is more of a usage guideline matter.

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 lists the style options ('geographical, poetic, material, literary, botanical, industrial, or mixed'), which mirrors the schema's style property but adds no extra meaning. It does not elaborate on parameters like market, n_names, or product_type, relying on the schema to carry that information.

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's function: 'Generate memorable, archive-verified colour names for any hex value.' It uses a specific verb ('Generate'), a specific resource ('colour names'), and adds scope ('for any hex value') and differentiators ('archive-verified', 'grounded in a real archive source'). This distinguishes it from sibling tools like colour_card or palette_generate, which focus on palettes or analysis rather than naming.

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 a clear context: 'The core of the Shopify product naming use case,' implying when the tool is appropriate. However, it does not explicitly mention when not to use it or compare it to sibling naming tools like ecommerce_namer, so it lacks exclusions or alternatives.

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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