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Generate Palette Swatch Image

palette_swatch
Read-only

Generate a clean, text-free PNG swatch image from hex colours. Returns a URL to the PNG. Use for Midjourney --sref style references or design mood boards. Supports photo-proportional weights from palette extraction, equal distribution, grid layout, a true smooth LCh-interpolated gradient (no hard colour edges, best for mood/atmosphere/colour-grade references rather than literal composition), and 13 fixed design ratios (6310, 7020, triptych, quad, filmstrip, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hNoOutput height in pixels (default 630)
wNoOutput width in pixels (default 1200)
hexesYesComma-separated hex values e.g. #d4a829,#1a5c6e,#0a0a0b
layoutNophoto | equal | grid | gradient | filmstrip | 6310 | 7020 | 5030 | 8010 | 5050 | 6040 | triptych | quad | 55-25-20 | quint | 70-10-20 | 40-30-20-10 | 33-33-24-10. gradient is a true smooth perceptual blend with no hard edges, unlike every other layout here.
weightsNoComma-separated proportional weights from k-means extraction. Used only when layout=photo.

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?

Annotations already state readOnlyHint=true, and the description adds useful behavioral detail: it returns a URL to a PNG, and the gradient layout is a 'true smooth LCh-interpolated gradient' without hard edges. This goes beyond the annotations without contradicting them, though it doesn't mention potential failures or rate limits.

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 front-loaded with the main action ('Generate a clean, text-free PNG swatch image'), followed by the output format and use cases. Every sentence carries meaning—no filler or redundancy.

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 5 parameters, 100% schema coverage, read-only annotation, and presence of an output schema, the description sufficiently covers purpose, usage context, and key behavioral nuances. It provides enough information for an agent to select and invoke the tool correctly.

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?

The input schema covers all parameters with descriptions (100% coverage), so the description isn't required to explain each parameter. It does add a helpful summary of layout families ('13 fixed design ratios') but doesn't significantly expand beyond the schema's parameter descriptions, meriting the baseline score.

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 clearly states the tool's core function: generate a clean, text-free PNG swatch image from hex colours and return a URL. It also distinguishes the tool from siblings by naming specific use cases like Midjourney style references and mood boards, and by outlining distinctive layout capabilities (gradient and ratios).

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 explicitly recommends the tool for Midjourney --sref references or design mood boards, and advises when to use the gradient layout over others (best for mood/atmosphere, not literal composition). While it doesn't name alternative tools for exclusions, it gives clear context for when 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