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Prepare Local Garment Upload

prepare_local_garment_upload

First step for garment images on the host's local filesystem, such as files in ~/Downloads. Use only when the host can read those local files and perform the returned direct multipart HTTP uploads, such as Codex. Include every returned form field and send the file bytes under the returned file_field. Never send a local path to Uwear. Do not use this for ChatGPT attachments or existing public URLs. After every upload succeeds, call finish_local_garment_upload with the returned upload handles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesYesLocal image metadata in a stable order. The local paths never leave the client.

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses key behavioral traits beyond the annotations: the returned form fields and file_field must be used, local paths must never be sent to Uwear, and a follow-up call is required. This adds essential context about the two-step protocol and response handling, compensating for the lack of an output schema.

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?

Every sentence in the description serves a distinct purpose: scoping, prerequisites, execution details, security constraint, exclusions, and next-step instruction. It is dense yet efficient, with no redundant or filler content, and the key action is front-loaded.

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?

Given the tool's complexity (two-step upload process) and the absence of an output schema, the description is remarkably complete. It tells the agent exactly what to do with the response, what to avoid, and what to call next, covering all necessary context for successful invocation.

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?

The input schema already provides comprehensive descriptions for each parameter (file_name, content_type, content_length) with constraints like max length and MIME type enums. The description adds no new parameter-level meaning—it only restates the local path rule already embedded in the schema—so the baseline of 3 for high schema coverage 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 role as the first step for uploading garment images from the host's local filesystem, using specific verbs like 'prepare' and identifying the resource. It also differentiates from sibling upload tools by explicitly excluding ChatGPT attachments and public URLs, making its purpose unambiguous.

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 provides explicit conditions for use: only when the host can read local files and perform direct multipart HTTP uploads. It also states exclusions ('Do not use this for ChatGPT attachments or existing public URLs') and directs the agent to call finish_local_garment_upload afterward, offering clear guidance.

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.

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