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Propose Outfits

propose_outfits
Read-onlyIdempotent

Use Uwear's outfit proposer to create styled outfit combinations from selected garments. Returns candidate titles, clothing_item_ids, and short rationales; call create_outfit_from_garment_ids only after the user chooses a proposal to save.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_outfitsNoMaximum number of outfit proposals to return
instructionsNoOptional styling brief, occasion, season, constraints, or vibe
clothing_item_idsYesAccessible clothing item IDs to combine into outfit proposals

TDQS

A4.2/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds behavioral value by disclosing return contents ('candidate titles, clothing_item_ids, and short rationales') and the correct follow-up action, which are not present in annotations. It doesn't contradict any 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?

Two sentences with no redundant phrases. The first sentence introduces the tool's core purpose, and the second provides return details and workflow. Every word contributes essential information, making it efficiently front-loaded.

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?

The description covers the tool's purpose, return payload, and the necessary next step (calling create_outfit_from_garment_ids). Since there is no output schema, describing return contents is essential and handled well. It doesn't detail edge cases or advanced behaviors, but those are unlikely needed for a straightforward proposal tool.

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 coverage is 100%, with all three parameters (clothing_item_ids, max_outfits, instructions) already described in the input schema. The description adds no additional parameter-specific meaning, so the baseline score of 3 is appropriate.

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's function: 'use Uwear's outfit proposer to create styled outfit combinations from selected garments.' It identifies the specific resource (outfit proposals) and action (propose), and distinguishes itself from sibling tools like create_outfit_from_garment_ids by explicitly reserving that tool for a later saving step.

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 provides clear usage context: it tells the agent to call this tool to generate proposals and to call create_outfit_from_garment_ids only after the user chooses one. It doesn't explicitly list alternative tools or say when not to use it, but the workflow guidance is strong enough to guide tool selection.

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

A3.5/5.0
Disambiguation3/5

Most tools target distinct resources and have detailed descriptions, but several closely related families exist: create_credit_checkout_session vs mcp_create_credit_checkout_session, the propose_brief/confirm_brief/update_brief lifecycle, and the many avatar/upload entry points. An agent must read long caveats carefully to avoid selecting the wrong tool.

Naming Consistency4/5

The vast majority of tool names follow a predictable snake_case verb_noun pattern (list_*, get_*, create_*, update_*, propose_*). The mcp_* prefix group and varied creation verbs (create/upload/save/add/generate) are minor deviations, though mcp_create_credit_checkout_session duplicating create_credit_checkout_session adds some confusion.

Tool Count1/5

With 67 tools, this is an extreme mismatch by the rubric's own 50+ threshold, far beyond the typical 3-15 well-scoped range. Many tools are narrow lifecycle steps such as two-phase local uploads, app-only montage internals, and multiple ArtDirection authoring variants, making the agent-facing surface very heavy.

Completeness3/5

The core generation, brief, montage, and QA workflows are covered thoroughly with polling and result retrieval. However, notable lifecycle gaps exist: outfits, locations, avatars, and tags mostly have create/list/get but no update or delete, and delete_template is the only delete tool in the entire set.

Resources