Colour Memory

Iterate and Refine a Palette

palette_iterate

Read-only

Refine an existing palette using natural language feedback. Submit your current palette and feedback such as more melancholic, too corporate add warmth, or better for Gen Z luxury. Returns a refined palette with archive grounding and change rationale. 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
`markets`	No	Target markets
`palette`	Yes	Current hex palette to refine
`feedback`	Yes	Natural language refinement e.g. more melancholic
`use_case`	No	Use case context e.g. luxury homewares
`direction`	No	Alias for feedback — natural language direction e.g. more dangerous, more historical, warmer
`n_results`	No	Number of variants to return (default 1)

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, but the description adds meaningful behavioral context beyond that: the result carries rendered palette and PNG/PDF/ASE/JSON/CSS downloads, the agent should show them to the customer, and archive anchors must not be presented as recommended colours. It also routes to palette_finalize for a custom final choice. These are non-obvious and valuable behavioral disclosures.

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 dense but every sentence earns its place: core action, return contents and presentation, a warning about archive anchors, and the follow-up call to palette_finalize. It is front-loaded with the main purpose and wastes no 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?

Given the output schema exists and parameter schema coverage is 100%, the description does not need to re-explain return values or every parameter. It covers the workflow, what to do with the result, and an important correctness rule. Nothing critical is missing for correct invocation and downstream behaviour.

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 already documents all six parameters. The description adds some colour to 'feedback' with examples like 'more melancholic' and 'too corporate add warmth,' but it does not materially clarify markets, use_case, direction, or n_results beyond what the schema already provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's verb and resource: 'Refine an existing palette using natural language feedback.' It also specifies what is returned: a refined palette with archive grounding and change rationale. It does not explicitly contrast itself with similar siblings like palette_generate or palette_variants, but the 'existing palette + feedback' framing makes the purpose fairly distinguishable.

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 gives a clear usage context: submit your current palette plus natural-language feedback, and the tool returns a refined palette. It also provides a conditional follow-up instruction to call palette_finalize when the agent selects its own final palette. However, it does not explicitly state when not to use this tool or name direct alternatives.

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

A3.6/5.0

Disambiguation2/5

Several tool clusters perform near-identical functions: extract_image_colours, image_palette and palette_extract all extract dominant colours from images; colour_passport, colour_dna, colour_metrics and colour_cultural_risk all profile a single hex; and at least six compound 'complete package' tools (agent_brief, archive_report_brief, brand_report, design_session, image_brief, session_brief) overlap heavily in scope. The descriptions try to differentiate -- some even point to tool_guide for routing -- but the volume and similarity of clusters makes misselection likely.

Naming Consistency4/5

The vast majority of tools follow a clear domain_prefix_suffix pattern (colour_, palette_, archive_, brand_, accessibility_, project_) and within families naming is very disciplined (brand_guideline_specify/select/pdf/claims/status, project_get/list/versions/delete). However, a handful of outliers invert the order (extract_image_colours, ingest_image, render_colour_result, query_hex) and some descriptions reference tools that don't exist as endpoints (palette_from_concept, match_paint_system, get_colour_metrics).

Tool Count1/5

88 tools is far beyond any reasonable single-server surface, even for a platform spanning archives, branding, interiors and accessibility. The sheer number forces agents into a massive decision space, and many tools exist purely as convenience wrappers that replace chains of 3-6 other tools, suggesting aggressive consolidation was needed.

Completeness4/5

The surface covers an unusually broad domain -- archive search, colour science, palettes, branding, interiors, accessibility, image extraction, projects, and PDF/Word/Excel exports -- with very few dead ends for end-user workflows. Minor gaps: several compound-tool descriptions reference tools that no longer exist, and valid archive names are only discoverable via error messages rather than a dedicated listing endpoint.

Resources

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