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Uwear

by ai.uwear

Generate Montage Clip

mcp_generate_clip
Destructive

App-only: queue an image-to-video generation for a montage clip. Spends credits, so it is hidden from host LLMs; the montage editor calls this when the user presses Generate on a draft clip. Returns the generation_id to poll via get_generation_status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesVideo model slug or supported model name
promptNoOptional motion prompt for the video clip
durationNoVideo duration in seconds; defaults to 5
image_urlNoHTTPS source image URL; provide exactly one source
resolutionNoOptional output resolution; defaults to 480p
generate_audioNoWhether to generate audio with the video
last_frame_urlNoOptional final-frame image URL for supported video models
uploaded_file_idNoUploaded file ID to use as the source image; provide exactly one source
generation_result_idNoExisting generation result ID to use as the source image; provide exactly one source

TDQS

A4.2/5.0
Behavior4/5

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

The description adds behavior beyond annotations: it spends credits, is app-only/hidden from host LLMs, queues the generation, and returns a generation_id to poll. This complements the destructiveHint annotation without contradicting it.

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 with no wasted words. The most important information is front-loaded, followed by the credit-spending caveat and the return value in a logically ordered way.

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 queue-style tool with 9 schema-documented parameters, the description sufficiently explains purpose, usage restrictions, side effects, and how to follow up. The lack of an output schema is mitigated by explicitly mentioning the generation_id and the polling endpoint.

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%, and each parameter already has descriptive text. The description adds no parameter-specific meaning beyond the general image-to-video context, so the baseline 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 queues an image-to-video generation for a montage clip. It names the specific action ('queue'), the resource ('montage clip'), and scope ('App-only'), distinguishing it from sibling tools like propose_montage and mcp_export_montage.

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?

It explicitly says when to use it: the montage editor calls this when the user presses Generate on a draft clip, and it is hidden from host LLMs, providing a clear when-not. It does not name alternative tools, but the context is strong.

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