Colour Memory

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

Search Colours by Concept or Culture

query_conceptual

Read-only

Ask a cultural, historical, or material colour question. Returns named archive colours with provenance and cultural context. Works for abstract queries like 'grief' (returns Ophelia Violet, Titan's Grief), 'toxic Victorian pigments' (returns Nightshade Poison Berry Purple, Victorian Arsenic Mirror Silver), or 'the sea at dusk' (returns Evening Sea, Thames Blue Dusk). Even a vague or personal-sounding query never comes back empty -- result_path in the response tells you honestly whether this was a 'direct' hit, a 'broadened' retry (relaxed match threshold), or a 'redirected' fallback (dropped an archive restriction or switched to full-corpus semantic search).

Input Schema

TableJSON Schema

Name	Required	Description
`query`	Yes	The colour concept or cultural question to search for
`archive`	No	Optional: restrict to a named archive e.g. 'Japan', 'Pigment', 'OttomanEmpire'
`n_results`	No	Number of results (default 5)

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, it discloses fallback behavior: result_path honestly reports direct/broadened/redirected outcomes, and 'never comes back empty' reveals relaxed matching. This is meaningful behavioral context not captured in annotations.

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?

Four sentences, each carrying distinct value: purpose, examples, and fallback transparency. No filler; front-loaded with the primary intent.

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?

With a full output schema and 100% parameter docs, the description need only cover behavior and use cases, which it does thoroughly. It could add explicit sibling comparisons, but that's optional given its clarity.

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 already documents all 3 parameters with examples and defaults (100% coverage). The description adds context about 'archive restriction' but does not elaborate on parameter syntax beyond the schema, so it meets the baseline 3.

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 first sentence states a specific verb+resource ('Ask a cultural, historical, or material colour question') and clarifies returns ('named archive colours with provenance and cultural context'). Concrete examples ('grief', 'toxic Victorian pigments') distinguish it from sibling query_hex or archive_search.

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?

Examples demonstrate ideal query types ('abstract queries like grief'), and the phrase 'Even a vague or personal-sounding query never comes back empty' implies tolerance for ambiguity. However, it does not explicitly name alternatives or state when NOT to use it, stopping short of a 5.

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

A3.7/5.0

Disambiguation2/5

Multiple tool clusters have unclear boundaries: colour_passport, colour_dna, colour_metrics, and colour_cultural_risk overlap heavily; palette_extract, extract_image_colours, image_palette, and ingest_image cover similar image-colour extraction territory; and compound tools like design_session, image_brief, session_brief, and archive_report_brief duplicate chains of component tools. The descriptions are detailed and occasionally state 'use this instead of X', but an agent still faces many near-duplicate choices.

Naming Consistency3/5

Names are consistently snake_case and often use domain prefixes (colour_, palette_, archive_, brand_, accessibility_), but verb placement is mixed: some are verb_noun (extract_image_colours, query_hex, style_match), others are noun_verb (colour_dna, palette_generate, brand_audit), and a few standalone names (ui_states, tool_guide, meta_capabilities) don't fit either pattern. The convention is readable but not uniform.

Tool Count1/5

At 88 tools, this is far beyond the reasonable well-scoped range and exceeds the 50+ extreme mismatch threshold. Many tools are compound wrappers that consolidate chains of simpler tools, adding redundancy and cognitive load rather than genuine coverage.

Completeness5/5

For the apparent domain, the tool surface is extremely comprehensive: colour extraction, analysis, naming, accessibility, cultural/provenance research, palette generation, brand systems, interior design, ecommerce copy, image briefs, project lifecycle, exports, and diagnostic tools are all present. Persistent objects have list/get/versions/delete/export support, so there are no obvious dead ends.

Resources

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