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Uwear

by ai.uwear

Generate Avatar Reference

generate_avatar
Destructive

Generate reusable avatar/model references through the same creator path as the Uwear app. The default view is upper_body_front, a clean bust identity anchor. For a draft multi-view avatar, generate the bust first, pass its result URL in identity_reference_urls when generating each slot view, then save the chosen results together with save_generated_avatar. Traits are optional user-given facts and are never guessed. If the user wants help writing the person description, call build_avatar_prompt first only for that purpose; if the user's wording is already intentional, pass it directly here.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional name to carry with the avatar generation
viewNoReference view to generate. Defaults to the upper-body identity anchor.upper_body_front
buildNoOptional concrete physical build. Never inferred.
modelNoOptional image model slug or display name. Defaults to the cheapest active image generation model available to the account.
promptYesDescription of the person to render as a reusable avatar reference
age_rangeNoOptional concrete age or age range. Never inferred.
avatar_idNoOptional existing avatar whose anchor fixes identity for a slot view. Not required when identity_reference_urls are supplied.
height_cmNoOptional height in centimeters. Never inferred.
num_imagesNoNumber of avatar candidate images to generate
identity_reference_urlsNoPerson-image URLs that fix identity for a slot view. In a draft flow, pass the generated bust URL here before the avatar has been saved.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations include destructiveHint=true, so the agent is already aware of side effects. The description adds valuable behavioral context: default view, traits are never guessed, and identity_reference_urls flow for draft consistency. No contradictions 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, front-loaded with purpose, followed by concise workflow and trait-handling guidance. No wasted words.

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 complex 10-param tool with no output schema, the description covers purpose, workflow, trait rules, and alternative routing. It doesn't explicitly detail return format, but mentions result URL and save flow, while annotations cover destructiveness.

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 has 100% description coverage, so baseline is 3. The description adds workflow meaning for identity_reference_urls (pass bust URL before saving) and reinforces trait parameters as user-supplied facts, exceeding 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?

The description uses a specific verb ('Generate') and resource ('reusable avatar/model references') and clarifies the context ('through the same creator path as the Uwear app'). It distinguishes the tool from siblings like build_avatar_prompt (prompt writing) and save_generated_avatar (saving results).

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 provides when to use the tool directly versus calling build_avatar_prompt, and outlines a multi-step workflow for draft avatars, mentioning save_generated_avatar as a follow-up. This meets the 'when/when-not/alternatives' criterion.

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