Skip to main content
Glama

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
NameRequiredDescriptionDefault
marketsNoTarget markets
paletteYesCurrent hex palette to refine
feedbackYesNatural language refinement e.g. more melancholic
use_caseNoUse case context e.g. luxury homewares
directionNoAlias for feedback — natural language direction e.g. more dangerous, more historical, warmer
n_resultsNoNumber of variants to return (default 1)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.3/5.0
Behavior5/5

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

Despite readOnlyHint=true covering the safety profile, the description adds substantial behavioral context: the return includes 'archive grounding and change rationale', the result 'already carries the rendered palette and its PNG, PDF, ASE, JSON and CSS downloads', and — critically — a warning about a known failure mode: 'Never present the archive anchors a colour was derived from as the colours you are recommending.' The palette_finalize routing also defines follow-on behavior. This far exceeds what annotations alone provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than minimal (about 4 sentences) but every sentence carries operational weight: core purpose with examples, return contents, download handling instruction, a critical caution, and a workflow handoff. It is front-loaded with the core function in sentence one. Slightly denser than strictly necessary, but the extra length is justified by the failure-mode warning and sibling routing that would otherwise be absent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 100% schema coverage, an output schema, and readOnlyHint annotation, the description is nearly complete: it covers purpose, return contents, downloads, a key caution, and follow-on workflow. The one genuine gap is the interplay between n_results (multiple variants possible) and the instruction to 'call palette_finalize once with those exact colours' — it is ambiguous whether finalize should receive a single chosen variant or all returned variants.

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 all 6 parameters and the baseline is 3. The description adds marginal value by giving concrete natural-language feedback examples ('more melancholic, too corporate add warmth, or better for Gen Z luxury') that illustrate the intended tone of the feedback parameter, but it does not substantively clarify other parameters beyond what the schema already states.

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 opening sentence states a specific verb+resource+method: 'Refine an existing palette using natural language feedback.' It clearly distinguishes itself from siblings like palette_generate (creating new palettes), palette_finalize (committing a final choice), and palette_analyse (analyzing). The examples of feedback types further pin down what the tool does.

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 clear context for use — you call this when you have a current palette and natural language feedback to refine it. It explicitly routes to an alternative: 'If you go on to choose a final palette OF YOUR OWN from this evidence, call palette_finalize once with those exact colours.' It lacks an explicit when-not statement (e.g., 'use palette_generate for new palettes'), but the 'existing palette' constraint plus the sibling routing makes usage conditions clear.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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