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burn_subtitles

Hardcode subtitles directly into video frames with adjustable font size, colors, outline, and position. This ensures captions display consistently on TikTok, Instagram Reels, and YouTube Shorts.

Instructions

Hardcode/burn subtitles directly into video frames with customizable font size, colors, and positioning. Unlike soft subtitles, burned subtitles render on all platforms including TikTok, Instagram Reels, and YouTube Shorts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marginVNoVertical margin distance in pixels from the bottom edge. Default: 35.
fontSizeNoSubtitle font size in points. Default: 24.
fontColorNoPrimary subtitle text color: 'white', 'yellow', 'cyan', or ASS hex e.g. '&H00FFFFFF'. Default: 'white'.white
videoPathYesPath to the source video file.
outputPathYesTarget output video path (.mp4, .mov, or .mkv).
outlineColorNoSubtitle border/outline color: 'black', 'white', or ASS hex. Default: 'black'.black
outlineWidthNoOutline thickness in pixels. Default: 2.
subtitlesPathYesPath to the subtitle file (.srt, .vtt, or .ass) to burn into the video.
hardwareAccelerationNoUse hardware-accelerated video encoding when available. Default: true.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.0

TDQS

A3.5/5.0
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 discloses that subtitles are physically burned into frames and render across platforms, but it omits important behavior such as whether the source video is preserved, overwrite behavior, processing time, permissions, and error handling.

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 two focused sentences with zero waste. The core action is front-loaded, and the platform compatibility note provides useful supporting context without being verbose.

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?

For a 9-parameter media-processing tool with no annotations and no output schema, the description covers purpose and platform benefit, and the schema fully documents parameters. However, it does not describe return values, source/output file behavior, or processing side effects, leaving some contextual gaps.

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 has 100% description coverage, so all nine parameters are already documented. The description mentions font size, colors, and positioning generally, but adds no parameter-level detail beyond what the schema provides, making the baseline score appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action and target: 'Hardcode/burn subtitles directly into video frames.' It also distinguishes burned subtitles from soft subtitles conceptually, though it does not explicitly name the sibling tool add_subtitles.

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

Usage Guidelines4/5

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

It gives clear usage context by explaining that burned subtitles render on all platforms, including TikTok, Instagram Reels, and YouTube Shorts. However, it does not explicitly direct users to add_subtitles for soft subtitles or state exclusions and prerequisites.

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