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Arkeonix CapCut MCP

Arkeonix CapCut MCP

by daviilpzDev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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capcut_generate_chapters

Generate structured YouTube and social media chapter markers from scene analysis, turning video drafts into timestamped, navigable segments for improved viewer engagement.

Instructions

Generate structured YouTube / Social Media chapter markers and timestamps from scene analysis.

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>"
    },
    "name": "capcut_generate_chapters"
  }
}

Input Schema

NameRequiredDescriptionDefault
draftYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It indicates the source and output type, but does not disclose whether this mutates the draft, overwrites existing chapters, requires a saved draft, or returns data. The word 'Generate' hints at producing markers, but side effects are opaque.

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, front-loaded sentence with no filler or repetition. It states the action, target, and data source efficiently.

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?

Despite a simple one-parameter schema, the tool has no output schema and no annotations, so the description must clarify parameter use and return behavior. Both are absent; an agent would not know what to pass as 'draft' or what to expect back.

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

Parameters1/5

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

Schema description coverage is 0% and the description never mentions 'draft', its format, or whether it is an ID, path, or serialized draft. The parameter name gives only a weak clue and the description adds no semantic value beyond 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 states a specific verb ('Generate'), a concrete resource ('structured YouTube / Social Media chapter markers and timestamps'), and a source ('from scene analysis'). It is clearly distinct from the sibling tools, none of which target chapter generation.

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 phrase 'from scene analysis' implies this tool should be used after scene analysis has been performed, but the description gives no explicit when-to-use/when-not-to-use guidance and names no alternatives. Usage must be inferred rather than stated.

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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