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capcut_add_proof_overlay

Add Amazon review screenshots, PubMed study proofs, or TikTok comment bubbles as overlays on CapCut drafts. Position and scale them to enhance video credibility.

Instructions

Add an Amazon review screenshot, PubMed study proof, or TikTok comment bubble overlay.

position: "top_right", "top_left", "center_pop", "bottom_left"
scale: size relative to 1080x1920 canvas (default 0.45)

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_name": "<draft_name>",
      "asset_path": "<asset_path>",
      "start_ms": "<start_ms>",
      "duration_ms": "<duration_ms>"
    },
    "name": "capcut_add_proof_overlay"
  }
}

Input Schema

Name	Required	Default
`scale`	No
`position`	No	top_right
`start_ms`	Yes
`asset_path`	Yes
`draft_name`	Yes
`duration_ms`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 11, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It does not disclose that this is a mutation tool, what draft state is required, how asset_path or timing interact with the draft, whether the operation is reversible, or any permission or rate-limit considerations. The position and scale details add some useful behavior but leave major gaps.

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?

The description is short, front-loaded with the core purpose, and uses bullets for parameter-specific details. Every sentence and bullet earns its place, with no redundant wording.

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?

Although an output schema exists and return values need not be explained, the description is incomplete for a 6-parameter mutation tool with no annotations. It omits required-parameter semantics, usage conditions, and behavioral details, leaving an agent with substantial gaps.

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 0%, and the description only explains position values and scale relative to a 1080x1920 canvas. It does not explain the required parameters draft_name, asset_path, start_ms, or duration_ms, which are central to correct invocation.

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 action ('Add ... overlay') and resource ('proof overlay'), with concrete examples such as an Amazon review screenshot, PubMed study proof, or TikTok comment bubble. This clearly differentiates it from sibling tools like caption, hook-badge, and punch-zoom tools.

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?

It gives no explicit guidance on when to use this tool versus alternatives such as capcut_add_hook_badge or capcut_add_captions. The examples imply a social-proof use case but do not state conditions, exclusions, or prerequisites.

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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