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Clip a video into shorts

clip_video

Queue clipping for an already-imported video_id (from list_videos or get_import_status). Uses the monthly shorts quota. When moment_ids is set, those moments are clipped in order and count is ignored (quota = moment_ids.length). Otherwise count random GPT moments are clipped (default 5). Supports theme, crop_style, short_format, starter_template_id, enhance toggles, and style overrides. Returns a job_id; poll get_clip_status.

Input Schema

NameRequiredDescriptionDefault
zoomNoAuto zoom animations
countNoNumber of clips when moment_ids is omitted (default 5). Ignored when moment_ids is set.
styleNoOptional caption style overrides (font, colors, position, emojis, etc.)
themeNoCaption theme id from list_themes (default 18)
brollsNoAuto B-rolls
silentsNoCut silent parts
video_idYesSendShort videos.id (already imported)
translateNoTranslate captions
crop_styleNoCrop layout: none | blur | basic | caption | rounded-square
moment_idsNoSpecific video_moments ids to clip (from list_moments). When set, count is ignored.
transitionsNoScene transitions
short_formatNoOutput aspect: 9:16 | 16:9 | 1:1 | 4:5
audio_effectsNoSubtitle audio effects
translateListNoTarget language codes when translate is true
starter_template_idNoStarter template id from list_templates. Omit to use the account's default template. null for no template

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare it is a non-readonly, non-idempotent, non-destructive open-world write, so the safety bar is covered. The description adds genuinely new operational context: it consumes the monthly shorts quota, the quota equals moment_ids.length when specified, and it returns a job_id for async polling rather than finished shorts.

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?

Five dense sentences, all front-loaded: identity and prerequisites first, the moment_ids/count branch next, optional knobs, then the return-and-poll instruction. No sentence is filler.

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?

Given 15 parameters, a nested style object, and no output schema, the description covers the high-risk gaps: quota cost, async job_id, and polling target. It omits edge behavior (what happens if the video isn't finished importing, error semantics) but is sufficient for a correct first call.

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 coverage is 100%, so the baseline is 3; the description still adds value by explaining the precedence rule between moment_ids and count (and the resulting quota calculation) in one place. Defaults like count=5 and theme=18 are already in the schema, so the added semantics are real but incremental.

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?

Specific verb (queue clipping) plus resource (an already-imported video_id), and it names where the id comes from (list_videos / get_import_status). It also implicitly separates itself from get_clip_status by casting itself as the enqueue step, so an agent can distinguish it from siblings without opening schemas.

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 gives clear operating rules: use moment_ids to clip specific moments in order, otherwise count random moments, and poll get_clip_status afterward. It doesn't state when to prefer a sibling like create_music_video, but the context for calling this tool is unambiguous.

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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