Skip to main content
Glama

Uwear

by ai.uwear

Upload Avatars from Chat

upload_avatar_from_chat_file

Batch upload reusable avatars/models from person photos the user attached in chat. Each photo becomes the upper_body_front identity anchor. Keep names and optional user-given age_range, build, and height_cm in avatars[], then pass top-level image_file_1, image_file_2, etc. in the same order. Traits are never guessed. No base64, no local paths. Do not use ordinary text-to-image results as an upload fallback; use generate_avatar followed by save_generated_avatar for Uwear-created avatars. Use only when the user explicitly wants a specific or consistent person.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoSingle recovered avatar/model name
buildNoOptional concrete physical build. Never inferred.
avatarsNoAvatar/model metadata in the same order as image_file_1, image_file_2, etc. No base64.
age_rangeNoOptional concrete age or age range. Never inferred.
file_nameNoSingle recovered original file name
height_cmNoOptional height in centimeters. Never inferred.
image_urlNoSingle resolved download URL used by automatic opaque-file recovery.
mime_typeNoSingle recovered MIME/content type, e.g. image/png
descriptionNoSingle recovered avatar metadata
image_file_1NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.
image_file_2NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.
image_file_3NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.
image_file_4NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.
image_file_5NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.
image_file_6NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.
image_file_7NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.
image_file_8NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.
image_file_9NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.
image_file_10NoHost-provided uploaded file reference for the matching item index when it has no image_url. Use the file object supplied by the host, not a bare file_... ID.

TDQS

A4.6/5.0
Behavior4/5

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

The description adds behavioral context beyond the annotations: 'Each photo becomes the upper_body_front identity anchor' and 'Traits are never guessed.' It also discloses important input constraints ('No base64, no local paths'). The annotations only indicate non-read-only and non-destructive, so the description meaningfully fills gaps about how the tool behaves.

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 appropriately concise at four sentences, with each sentence serving a distinct purpose: stating the core function, describing the identity anchor, listing input constraints, and providing usage boundaries/alternatives. It is front-loaded with the primary action and contains no fluff or redundant details.

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 tool has 19 parameters and nested objects, but the description supplies enough high-level guidance for correct invocation: the batch nature, ordering needs, input types, and constraints. It does not explain return values, but with no output schema specified, this is acceptable. It also clearly differentiates from the generate_avatar flow, making it complete for an agent to decide when and how to use it.

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?

While the schema covers 100% of parameters, the description adds crucial relational semantics: 'Keep names... in avatars[], then pass top-level image_file_1, image_file_2, etc. in the same order.' This clarifies the critical alignment between the array and the file parameters, which is not obvious from individual schema descriptions. It also reinforces the 'No base64' constraint.

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 purpose: 'Batch upload reusable avatars/models from person photos the user attached in chat.' This is specific with a verb and resource, and it distinguishes from siblings like generate_avatar by specifying 'from person photos... attached in chat' and 'reusable avatars/models.' It also clarifies that each photo becomes an 'upper_body_front identity anchor,' which further defines the tool's role.

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 the tool ('Use only when the user explicitly wants a specific or consistent person') and when not to ('Do not use ordinary text-to-image results as an upload fallback'). It names the alternative tools: 'use generate_avatar followed by save_generated_avatar for Uwear-created avatars.' This provides clear, actionable guidance relative to sibling tools.

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

All Uwear 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

A3.5/5.0
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