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Create clip job

create_clip_job

Queue one video URL, or a batch of up to 10 video URLs, for AI clipping. This consumes the account's clip-minute allowance.

Input Schema

NameRequiredDescriptionDefault
urlNo
urlsNo
layoutNoblur
optionsNo
categoryNogeneric
numClipsNo
userPromptNo
aspectRatioNo9:16
maxDurationNo
minDurationNo
sourceTitleNo
captionStyleNoclassic
sourceChannelNo
idempotencyKeyNoRecommended: makes a retried call return the original job instead of queueing a second one.
sourceStoragePathNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior3/5

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

The annotations establish the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true), so the bar is lower. The description adds real behavioral value by disclosing that the call 'consumes the account's clip-minute allowance,' a billing side effect not captured by annotations, but it omits job/async semantics and failure behavior for a 15-parameter queuing operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with zero filler, and the core action plus its batch limit is front-loaded. Nothing is wasted, though there is little beyond the essentials.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 15 parameters, nested option objects, and no output schema, the description is far too thin. It should at least hint at the async job lifecycle (polling via get_clip_job_status) and the primary option groups, none of which appear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 7%, so the description must compensate and largely does not. It conveys the single-vs-batch behavior and the 10-item cap (matching url/urls), but says nothing about the remaining 13 parameters, including the nested options object with layout, mode, format, quality, brand_kit, and clip-count/duration controls.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Queue one video URL, or a batch of up to 10 video URLs, for AI clipping') with clear scope and the batch limit. However, it does not differentiate itself from siblings like get_clip_job_status, estimate_clone, or list_clip_jobs, which an agent would need to disambiguate an async job tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use guidance and no named alternative (e.g., checking allowance via get_clip_minutes first, or polling with get_clip_job_status). Usage is only implied by the verb 'Queue ... for AI clipping'.

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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