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Create clip

create_clip

Extracts a short vertical clip from a source video's time range, letting the model pick the best moment or keeping exact bounds. Queues preparation and render, then returns a clip ID to poll.

Instructions

Add a clip from a time range of the source (needs a transcript). mode exact keeps your bounds (3 s-10 min); mode ai lets the model pick the best moment inside them (≥8 s). Returns the new clip summary { id, status: "preparing", queued: "prepare", hint }; preparation (title, hook, scores) is queued and the render follows by itself (about a minute), so poll get_clip until done. To change an existing clip use update_clip instead.

Input Schema

NameRequiredDescriptionDefault
modeNoexact = these bounds (default); ai = best moment inside themexact
endSecYesRange end, seconds
startSecYesRange start, seconds from the start of the source video
projectIdYesProject id (from list_projects, search_clips or create_project)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.7.0
    • addedInput schema / properties / endSec / description
      Added value: +"Range end, seconds"
    • addedInput schema / properties / mode / description
      Added value: +"exact = these bounds (default); ai = best moment inside them"
    • addedInput schema / properties / projectId / description
      Added value: +"Project id (from list_projects, search_clips or create_project)"
    • addedInput schema / properties / startSec / description
      Added value: +"Range start, seconds from the start of the source video"
  2. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the whole burden and does so well: it discloses that the call is asynchronous, that preparation is queued, that rendering follows automatically and takes about a minute, and that the caller must poll get_clip. It does not cover failure modes or what happens if the range exceeds the stated limits.

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?

Every sentence earns its place and the core purpose comes first, but the single dense paragraph with stacked parentheticals ("(title, hook, scores)", "(about a minute)") makes it heavier to parse than necessary. No wasted claims, just tight packing.

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?

There is no output schema, and the description compensates by giving the exact return shape { id, status, queued, hint } plus the follow-up workflow via get_clip. Combined with the mode limits and the transcript prerequisite, an agent has everything needed to call this correctly on the first try.

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, but the description adds real constraints the schema lacks: a 3 s–10 min bound in exact mode and a ≥8 s minimum in ai mode. It does not add anything for projectId, startSec, or endSec beyond what the schema already states.

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?

Opens with a specific verb and resource ("Add a clip from a time range of the source") and immediately adds the prerequisite that the source needs a transcript. It distinguishes itself from update_clip and get_clip by name within the same paragraph.

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?

Spells out the two operating modes with their selection criteria (exact keeps your bounds, ai lets the model pick the best moment) and names the alternative for mutation ("To change an existing clip use update_clip instead"). The transcript prerequisite further tells the agent when this call will not work.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.