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Colour Memory

by io.github.DigbyO

Verify AI Image Generation Colour Fidelity

agent_verify
Read-only

Verify that an AI-generated image actually used the colours specified in an agent_brief call. Supply the generated image (URL or base64) and the target palette from agent_brief colour_tokens. Returns a fidelity score 0-100, dE2000 distance per colour, match quality per colour (accurate/acceptable/drifted/ignored), and an overall verdict. Use after agent_brief + image generation to close the colour loop.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_urlNoURL of the generated image
image_base64NoBase64 encoded generated image
target_paletteYesHex values from agent_brief colour_tokens e.g. ['#ED9921', '#E29937']

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 provide readOnlyHint=true; description does not contradict. Adds behavioral context by detailing output (fidelity score 0-100, dE2000 distance, match quality, verdict) and input flexibility (URL or base64), going beyond the bare 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?

Two sentences, each earning its place: first states purpose, second covers usage, inputs, outputs, and workflow position. Front-loaded and free of redundant phrasing.

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?

Output schema exists, so return values need no description. The description covers when to use it, what inputs to supply, what it returns, and where it fits in the agent_brief→image generation workflow. Complete for a read-only verification 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 has 100% coverage with descriptions for all three parameters. Description reinforces that target_palette comes from agent_brief colour_tokens, but adds no syntax or format details beyond what the schema already provides.

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?

Specific verb 'Verify' + resource 'AI-generated image' + scope 'colours specified in an agent_brief call'. Clearly distinguishes from siblings like colour_compare or palette_verdict by referencing agent_brief and the fidelity scoring workflow.

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?

Explicitly states 'Use after agent_brief + image generation to close the colour loop' – gives clear situational context. Does not name alternatives or exclusions, but the workflow context is unambiguous.

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