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Generate Montage Clip

mcp_generate_clip
Destructive

Queue an image-to-video generation for one montage clip. Spends credits: call it only after the user agreed in chat. Returns the generation_id to poll with get_generation_status. Prefer generate_montage_clips for a saved montage the user can open in Uwear.

Input 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_audioNoInclude audio in the delivered video. False delivers a silent file; fixed-audio providers are muted after generation at the same credits.
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

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / generate_audio / description
      Previous value: -"Whether to generate audio with the video"New value: +"Include audio in the delivered video. False delivers a silent file; fixed-audio providers are muted after generation at the same credits."
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and idempotentHint=false; the description adds the two things annotations cannot convey — that the call spends real credits and requires prior user consent in chat, plus that it is asynchronous and returns a generation_id for polling. It stops short of explaining duplicate-submission risk or what happens on retry, which is the main residual gap for a non-idempotent, credit-consuming call.

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 short sentences, each doing distinct work: what it does, the credit/consent gate, the return-and-poll contract, and the preferred alternative. The credit warning is front-loaded in the second sentence, before any call is made.

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?

Covers the essentials for a 9-parameter async mutation: cost, consent, return value, and polling path — with the input contract fully delegated to a 100%-covered schema and no output schema to explain. Minor gap: it doesn't state that repeated calls each incur cost (the practical consequence of non-idempotency).

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 description coverage is 100% and the per-parameter descriptions already cover defaults, the "exactly one source" constraint, and the audio muting behavior. The description adds nothing parameter-specific beyond restating that a source image is involved, so it sits at the baseline for fully documented schemas.

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?

States a specific verb and resource ("Queue an image-to-video generation for one montage clip") and immediately contrasts with the sibling generate_montage_clips. An agent can distinguish this single-clip queueing tool from the saved-montage batch tool without opening either schema.

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?

Gives an explicit precondition ("call it only after the user agreed in chat") tied to credit spend, and names the alternative ("Prefer generate_montage_clips for a saved montage the user can open in Uwear"). It also names the natural follow-up tool, get_generation_status, completing the workflow routing.

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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