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Browse example looks

browse_looks
Read-onlyIdempotent

Use when the user wants to see what is possible or pick a style before designing: the example looks (chrome, gold, glass, holographic, brushed metal, gradient, 3D text, animated), each with a rendered example image, what it is best for, the presets behind it, and an editor link that applies it. Works without an account. Do not use to get material ids for another tool; list_materials has the full catalogue.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
looksYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile needs no repetition. The description adds genuinely new behavioral context: no account is required, and each returned look bundles a rendered image, best-for use, underlying presets, and an editor link. No contradiction with annotations.

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?

Three sentences, each carrying distinct information: when to use, what results contain, and when not to use. The trigger is front-loaded first, and there is zero filler or repetition of schema/annotation content.

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?

For a zero-parameter read tool with an output schema and safety annotations already covering the risk profile, the description covers all agent-relevant decisions: when to call it, what it returns, auth requirements, and which sibling to use instead. Nothing material is missing.

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?

The tool has zero parameters and 100% schema coverage, so the baseline is 4 per the rubric; there is nothing for the description to document. The 'Works without an account' note implicitly signals no auth parameters are needed, which is a small bonus beyond baseline.

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?

States a specific verb ('browse') plus a concrete resource ('example looks') and enumerates the full content scope (chrome, gold, glass, holographic, brushed metal, gradient, 3D text, animated). The closing clause ('Do not use to get material ids... list_materials has the full catalogue') actively differentiates it from the sibling that would otherwise be confusable.

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?

Provides an explicit trigger condition ('Use when the user wants to see what is possible or pick a style before designing') and an explicit exclusion with a named alternative (list_materials for material ids). This is stronger routing guidance than most tool descriptions, matching the calibration HIGH example's structure.

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
Disambiguation3/5

Most tools target distinct resources, but several overlapping pairs exist: search/list_projects both find projects by title, fetch/get_project both return project details, and upload_logo_image/request_logo_image_upload are two upload paths. The descriptions help clarify boundaries, but an agent could still misselect.

Naming Consistency4/5

Tool names overwhelmingly follow a clear verb_noun snake_case pattern (create_, list_, get_, update_, delete_). Minor deviations like bare 'fetch' and 'search', plus the mixed '3d' in generate_3d_model vs '3D' in descriptions, keep it from being perfectly consistent.

Tool Count2/5

At 31 tools, this exceeds the 25+ threshold where agent tool selection becomes cognitively heavy. While the server covers a broad platform, several tools are near-redundant and could be consolidated, making the count feel inflated.

Completeness4/5

The surface covers project lifecycle, sharing/publishing, AI generation, uploads, materials, and account/plan management quite thoroughly. Minor gaps exist, such as no direct create_coin_project tool and no deletion for generation runs, but these are workable.

Resources