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Colour Memory

by io.github.DigbyO

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.

Input Schema

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

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?

The description adds behavioral context beyond the readOnlyHint annotation by explaining the scoring scale (1.00, 0.80, 0.50) and the nature of the computation. It does not contradict annotations and provides useful detail about what the tool evaluates and outputs.

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 compact and well-structured: it opens with a clear definition, then the scoring scale, input/output, usage context, and a concluding differentiation sentence. Every sentence earns its place without unnecessary wordiness.

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?

Given the tool's complexity, the description covers purpose, scoring semantics, input, output, and use cases. It also benefits from an output schema (though not shown), so return values are already documented. No critical gaps are apparent for an agent to decide when and how to invoke this tool.

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 adds an informal list of input fields ('name, hex, archive, source, notes') but this slightly mismatches the schema's 'primary_source' property. It does not add detail about the score_basis parameter beyond what the schema already includes.

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 states a specific action ('Score how tightly the material origin of a colour aligns with its social consequence') and names the resource ('Colour Memory's proprietary semantic metric'). It clearly distinguishes itself from sibling tools by emphasizing its uniqueness ('separates Colour Memory from palette generators').

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 usage context: 'Use for investigative reports, forensic briefs, museum content, editorial PDFs.' It implies a specialized analytical use case compared to palette generators, but it does not explicitly name alternative tools or state when not to use this tool.

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