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Arkeonix CapCut MCP

Arkeonix CapCut MCP

by daviilpzDev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

capcut_auto_jumpcut

Automatically slice and ripple-delete silence dead space on the main video track using speech intervals to produce clean jump cuts.

Instructions

Automatically slice and ripple-delete silence dead space on the main video track based on speech intervals.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "draft": "<draft>",
      "speechIntervals": "<speechIntervals>"
    },
    "name": "capcut_auto_jumpcut"
  }
}

Input Schema

NameRequiredDescriptionDefault
draftYes
paddingSecNosafety audio padding around words in seconds (default: 0.08)
trackIndexNotarget video track index (default: 0)
speechIntervalsYeslist of speech active intervals

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the destructive consequence ('delete') and mode ('ripple-delete'), which tells the agent the timeline will be altered. However, it does not mention reversibility, in-place draft modification, or side effects beyond the main video track.

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?

A single sentence, front-loaded with the action, with no filler. It conveys purpose, scope, and basis efficiently.

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

Completeness3/5

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

This is a destructive auto-edit tool with no annotations and no output schema, so the description should ideally warn about in-place modification and mention safety workflows such as cloning or discarding the draft. The current text explains the core operation but omits these operational guardrails, leaving a moderate gap.

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?

The schema already describes 3 of 4 parameters. The description adds high-level context by mapping 'speech intervals' to the speechIntervals parameter and 'main video track' to trackIndex, but it adds no detail on draft or paddingSec. This is adequate but does not significantly exceed the schema.

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 uses a specific verb phrase 'slice and ripple-delete' with a clear resource ('silence dead space on the main video track') and basis ('based on speech intervals'). It clearly communicates an automated jump-cut operation and is distinguishable from manual tools like capcut_delete_segment or capcut_trim_segment.

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

Usage Guidelines3/5

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

The description implies the use case: automatically remove silence from the main video track when speech intervals are known. It does not explicitly contrast with alternatives or state when not to use, but the 'automatically ... based on speech intervals' condition provides adequate contextual guidance.

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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