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Uwear

by ai.uwear

List Avatars

list_avatars
Read-onlyIdempotent

List the user's avatars: the reusable people who wear the garments, called 'models' or 'mannequins' in fashion terms (not the AI engines — for those, use list_models). Supports structured filters or query for hybrid name/metadata/image-attribute search. Set include_image_url=true when the avatar image itself is needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for pagination
sortNoSort order: 'field:direction', e.g. 'created_at:desc'. Default: created_at:desc
limitNoNumber of items per page
queryNoHybrid search across avatar/model names, metadata, and visual attributes.
tag_idsNoFilter to avatars with any of these tags
end_dateNoFilter: created on or before this date (ISO format)
avatar_idsNoFilter to specific avatar IDs
start_dateNoFilter: created on or after this date (ISO format)
include_image_urlNoSet true when you need to inspect or reuse the avatar/model image URL.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description goes beyond by explaining the domain semantics (avatars vs. AI models) and the hybrid search behavior, adding useful context that annotations do not convey.

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 focused sentences, each adding value: purpose, distinction from sibling, and key parameter guidance. No redundant phrasing or unnecessary detail.

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?

For a list tool with 9 optional params and no output schema, the description covers the essential context: what avatars are, the alternative tool, and the critical include_image_url scenario. Pagination and date filters are left to the schema, which is sufficient given the tool's simplicity. It could mention return format, but that's not critical here.

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%, so the baseline is 3. The description echoes the query parameter's hybrid search behavior and the include_image_url usage, but these are already described in the schema. It adds minimal new information beyond what structured fields provide.

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 opens with 'List the user's avatars', a clear verb+resource statement. It further clarifies that avatars are the reusable people/models/mannequins in fashion terms, and explicitly distinguishes from list_models for AI engines, which differentiates it from a likely sibling.

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?

It provides explicit guidance: 'not the AI engines — for those, use list_models' directly names the alternative. It also gives a concrete usage trigger: 'Set include_image_url=true when the avatar image itself is needed.' This tells the agent when to use the tool and how to configure a key parameter.

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