Colour Memory

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

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

Name	Required	Description
`image_url`	No	URL of the generated image
`image_base64`	No	Base64 encoded generated image
`target_palette`	Yes	Hex values from agent_brief colour_tokens e.g. ['#ED9921', '#E29937']

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior4/5

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

Annotations declare readOnlyHint=true, and the description adds behavioral detail by specifying the return format: fidelity score 0-100, dE2000 distances, match quality categories, and a verdict. This goes beyond the annotation to clarify what the tool computes and reports.

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 focused sentences cover purpose, inputs, outputs, and usage timing with no filler. The structure is front-loaded with the core action, making it easy to scan.

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?

Combined with a rich output schema and readOnly annotation, the description covers the tool's role in the workflow, input options, and output semantics. It leaves little ambiguity about when and how to use it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage so baseline is 3. The description adds nuance by indicating that the image can be supplied via URL or base64 (implying either/or), and ties target_palette to agent_brief colour_tokens, which is not explicit in the schema. This extra guidance supports correct invocation.

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 a specific action ('Verify') and a specific resource (AI-generated image colour fidelity against agent_brief). It distinguishes from siblings by referencing agent_brief colour_tokens and dE2000 metrics, making it unique among colour/palette tools.

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 instructs to use 'after agent_brief + image generation to close the colour loop', providing a clear usage context. It also describes what inputs to supply. However, it does not mention when not to use or name alternative tools, so it falls short of full exclusionary guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Colour Memory tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?