Skip to main content
Glama

capcut_assemble_ugc_video

Assembles UGC videos in one shot: improves voiceover audio, places avatar video, overlays B-roll, adds captions, and saves to CapCut Desktop drafts.

Instructions

One-shot UGC assembly:

  1. Pre-processes and improves voiceover audio (denoise, EQ, -14 LUFS).

  2. Places avatar video clip (with optional smart portrait background removal).

  3. Overlays B-roll clips on Track 2 per timestamps.

  4. Ingests and renders styled TikTok bold captions.

  5. Saves directly into CapCut Desktop draft folder for 1-click review/export.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draft_nameYes
captions_jsonNo
broll_clips_jsonNo
remove_avatar_bgNo
avatar_video_pathYes
improve_audio_modeNofast
voiceover_audio_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose substantive traits: a specific audio target (-14 LUFS, denoise, EQ), B-roll placement onto 'Track 2 per timestamps', and the side effect of writing into the CapCut Desktop draft folder. It omits error/overwrite behavior for an existing draft name, but adds real context beyond the schema.

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 numbered pipeline is front-loaded with a clear summary line, and every item is a distinct, non-redundant step. No filler or repetition.

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?

An output schema exists so return values need not be explained, and the pipeline sequence is well covered. However, with 7 parameters at 0% schema coverage and no annotations, the definition leaves a meaningful gap around argument formats that an agent would need to call it correctly.

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% across 7 parameters, so the description must compensate. It loosely maps concepts to parameters (voiceover/audio improvement, avatar clip, background removal, B-roll clips, captions) but never explains the expected JSON shape of captions_json or broll_clips_json, the values of improve_audio_mode, or path formats, leaving most parameters undocumented.

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?

States a specific verb and resource ('One-shot UGC assembly') and then enumerates the five concrete operations it performs (audio improvement, avatar placement, B-roll overlay, caption rendering, draft save). This clearly distinguishes it from atomic siblings like capcut_add_captions or capcut_add_video_clip, since it is the composite orchestrator.

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?

'One-shot' implies the use case is doing everything at once, but the description never explicitly states when to prefer this over chaining the atomic sibling tools, nor any preconditions or exclusions. Usage is inferable but not spelled out.

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