Skip to main content
Glama

Colour Memory

by io.github.DigbyO

Retrieve a Slow brand_guideline_specify Generation's Result

brand_guideline_status
Read-onlyIdempotent

Resolves a brand_job_ref returned by brand_guideline_specify when its race-to-complete window elapsed before generation finished. Read-only, in-process lookup -- never re-runs generation. Returns {"status": "processing"} if still running, {"status": "complete", "brand_ref": ..., "project_id": ..., "recommended_candidate_id": ..., "candidate_count": ...} once done (a compact summary -- use the returned brand_ref with brand_guideline_select/brand_guideline_pdf/brand_guideline_claims for full detail, the same pattern every other Brand Standard tool already uses), or {"status": "failed", "error_code": ..., "message": ...} if generation genuinely failed server-side. An unknown or expired brand_job_ref returns a structured BRAND_JOB_NOT_FOUND error, never a crash or empty success.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brand_job_refYesThe brand_job_ref returned by brand_guideline_specify's processing response.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive. Description adds behavioral context: it's an in-process lookup, never re-runs generation, and details all possible return states including error handling (BRAND_JOB_NOT_FOUND). This goes beyond the annotations and gives full transparency.

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 a single dense paragraph but every clause adds value: purpose, conditions, read-only nature, return shapes, error behavior, and pointers to related tools. It is front-loaded and free of filler.

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?

The description fully covers all possible outcomes (processing, complete, failed, not found) and how to proceed with the returned brand_ref. It references the output schema implicitly and the sibling tool family, making it complete for an agent to invoke correctly.

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 coverage is 100% with a clear parameter description. The tool description adds semantic context by explaining the parameter's origin ('returned by brand_guideline_specify') and its role in a race-to-complete scenario, which is not in the schema.

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 resolves a brand_job_ref from brand_guideline_specify when its race-to-complete window elapsed. It uses a specific verb ('Resolves') and resource ('brand_job_ref'), and distinguishes itself from siblings by focusing on status retrieval, not generation or full detail retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use it: when brand_guideline_specify's race-to-complete window elapsed before generation finished. It also provides guidance on alternatives by directing the user to use brand_guideline_select/brand_guideline_pdf/brand_guideline_claims for full detail, and clarifies it never re-runs generation.

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

All Colour Memory toolsInspect
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

A3.8/5.0
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