Colour Memory

Extract Dominant Palette Colours

palette_extract

Read-only

Extract dominant colours from an image using k-means++ clustering. Accepts a public image URL or base64-encoded image. Returns hex values with proportional weights sorted by luminance. Optionally runs palette_analyse on the results. Use this instead of image_palette when you need hex values with proportions for palette_analyse or palette_swatch. The result already carries the rendered palette and its PNG, PDF, ASE, JSON and CSS downloads -- show them to the customer. Never present the archive anchors a colour was derived from as the colours you are recommending. If you go on to choose a final palette OF YOUR OWN from this evidence, call palette_finalize once with those exact colours so the customer can see and download what you actually recommended.

Input Schema

TableJSON Schema

Name	Required	Description
`k`	No	Number of colours to extract (3-12, default 6)
`analyse`	No	If true, also run palette_analyse on the extracted colours and return archive names
`archive`	No	Explicit single archive name to restrict matching to e.g. 'MarsColour', 'Japan', 'Victorian'.
`image_id`	No	Ephemeral image_id from ingest_image (preferred for images over 50 KB)
`image_url`	No	Public URL of the image to extract colours from
`image_base64`	No	Base64-encoded image data (small images only, under 50 KB)
`grey_card_hex`	No	Hex value sampled from a grey or white card in the image for white balance correction e.g. #C8C8C8
`style_context`	No	Plain English style description that restricts archive matching to a coherent set e.g. 'English cottage garden', 'Victorian', 'Japanese', 'MarsColour', 'Arts and Crafts'. Prevents palette colours from being named across unrelated archives.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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

Despite readOnlyHint=true already covering the safety profile, the description adds substantial behavioral context: clustering method, input forms, luminance-sorted output, optional palette_analyse chaining, included download artifacts, and the warning not to present archive anchor colours as recommendations. This goes far beyond the annotation.

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 longer than average but front-loaded and dense; every sentence covers a distinct decision or caution. It balances capability, alternatives, output handling, and a critical safety instruction without wasted words.

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?

Together with a 100%-described schema and an output schema, the description covers the full workflow: what to call, why, what the result contains, what to show the customer, and what to do next if choosing an independent final palette. Nothing essential for correct invocation is missing.

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 schema fully documents the 8 parameters. The description mentions URL/base64 input and the analysis option, but does not add deeper meaning for parameters like k, archive, grey_card_hex, or style_context, so the baseline 3 is appropriate.

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 verb and resource: extract dominant colours from an image using k-means++ clustering, returning weighted hex values. It also explicitly distinguishes itself from image_palette, so an agent can select this tool without opening the schema.

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?

It explicitly says to use this tool instead of image_palette when hex values with proportions are needed for palette_analyse or palette_swatch. It also instructs when to call palette_finalize after making an independent final choice, leaving no ambiguity about follow-up routing.

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

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