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Colour Memory

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

index_resonance
Read-only

Colour Memory's proprietary semantic metric. Score how tightly the material origin of a colour aligns with its social consequence. 1.00 = material and consequence are indistinguishable (blood as prognosis, ash as finality). 0.80 = institution mediates the colour (paint as deterrence, flag as authority). 0.50 = symbolic or associative only. Input: list of colour entries with name, hex, archive, source, notes. Output: resonance score, material origin, social function, alignment reason, confidence. Use for investigative reports, forensic briefs, museum content, editorial PDFs. This is the metric that separates Colour Memory from palette generators.

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": {
      "entries": "<entries>"
    },
    "name": "index_resonance"
  }
}

Input Schema

NameRequiredDescriptionDefault
entriesYesList of colour entries to score for resonance
score_basisNoScoring basis (default: material_origin_to_social_consequence)

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

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 detail by defining the scoring scale (1.00, 0.80, 0.50) and listing output components (resonance score, material origin, social function, etc.). This goes beyond the annotation, though it doesn't disclose potential edge cases or error behavior.

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 concise and front-loaded with the core purpose. It includes the scoring thresholds and use cases, which are valuable, though the final marketing-style sentence ('This is the metric...') is slightly extraneous. Overall, every sentence contributes functional context.

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?

The tool has an output schema and annotations, so the description doesn't need to detail return values. It provides a complete picture of input, output, scoring logic, and use cases. Minor gap: no mention of how scores are calculated beyond ordinal thresholds, but that's acceptable given the output schema.

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 covers 100% of parameters with descriptions for 'entries' and 'score_basis'. The description restates the input structure ('list of colour entries with name, hex, archive, source, notes'), which mostly mirrors the schema and adds minimal additional semantics. It does clarify the default for score_basis ('material_origin_to_social_consequence') but this is already in the schema.

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?

Description clearly states a specific action ('Score how tightly the material origin of a colour aligns with its social consequence') on a defined resource (colour entries). It differentiates from simple palette tools by calling it a proprietary semantic metric, distinguishing it from siblings like palette_generate.

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

Usage Guidelines5/5

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

Explicitly lists intended use cases: 'investigative reports, forensic briefs, museum content, editorial PDFs.' It also implies when not to use by noting 'This is the metric that separates Colour Memory from palette generators,' steering agents away from basic colour generation tasks.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Colour Memory toolsInspect
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