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Colour Memory

by app.railway.up.colour-memory-api-production
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Mix Two Colours (Pigment Simulation)

colour_mix
Read-only

Simulate perceptually modelled subtractive mixing of two colours in CIE Lab space (not RGB screen blending). Returns the resulting mixed hex value and its nearest archive match with cultural context. Uses CIE Lab subtractive model for perceptual accuracy. Example: mixing Prussian Blue and Yellow Ochre gives a muted green; the tool identifies which archive colour that green most closely matches.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "hex_a": "<hex_a>",
      "hex_b": "<hex_b>"
    },
    "name": "colour_mix"
  }
}

Input Schema

NameRequiredDescriptionDefault
hex_aYesFirst colour hex e.g. '#003366'
hex_bYesSecond colour hex e.g. '#C8A600'
ratioNoMix ratio 0.0-1.0 where 0.5 is equal parts (default 0.5)

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • 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"
+}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "error": {
-      "type": "string"
-    },
-    "ok": {
-      "type": "boolean"
-    },
-    "result": {
-      "type": [
-        "object",
-        "array",
-        "string",
-        "null"
-      ]
-    }
-  },
-  "type": "object"
-}New value: +null
  3. Added

TDQS

A3.6/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=true, so the description usefully adds that the model is subtractive CIE Lab rather than additive RGB, and that output includes a mixed hex plus the nearest archive colour with cultural context. That is meaningful context beyond the annotation. It does not discuss determinism, precision, or edge cases like identical inputs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence is well front-loaded, but the third sentence ('Uses CIE Lab subtractive model for perceptual accuracy') largely restates the first, and the RGB disclaimer appears twice in spirit. The example is valuable but the wording could be tightened without losing meaning.

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?

An output schema exists, so return structure need not be re-explained, and the description still signals the two-part return (hex + archive match). For a 3-parameter read-only computation tool this is close to complete; only the ratio's effect on the result is left unaddressed.

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 coverage is 100%, so the schema already documents hex_a, hex_b, and the 0.0–1.0 ratio default. The description adds no further detail on how ratio weights the two pigments or what happens at the 0 and 1 extremes. Baseline 3 is appropriate when the schema carries parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource ('Simulate perceptually modelled subtractive mixing of two colours in CIE Lab space') and explicitly contrasts it with the naive alternative ('not RGB screen blending'). It does not differentiate itself from lookalike siblings such as colour_combination or colour_harmonies, so it stops short of a 5.

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

Usage Guidelines3/5

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

Usage is implied through the worked example (mixing Prussian Blue and Yellow Ochre) and the RGB disclaimer, which tells an agent this is the physically-plausible mixing path. However, there is no explicit 'use this when… / use X instead when…' routing against the many colour siblings, leaving selection to inference.

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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