VideoGenerationMCP

by AvivK5498

Overview Schema Source Related Servers Discussions Admin

Python

Local

burn_captions

Transcribe audio and burn word-timed captions on videos, with custom chunk options and Hebrew RTL support.

Instructions

Burn word-timed captions onto a video (local path, URL, or PiAPI task_id).

Default flow transcribes the video's OWN audio with ElevenLabs Scribe (word timestamps), groups words into short chunks (max_words per caption), and burns styled overlays: white bold, black stroke, drop shadow, centered low-third, Hebrew rendered in correct RTL visual order (English/brand tokens stay LTR). Pass captions ([{text, start, end}]) to skip Scribe and burn exact chunks; style overrides (font_px, y_pct, fill, crf, font_path...). Run AFTER verify_generated_audio passes and BEFORE stitch_videos.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "burn_captions"
  }
}

Input Schema

Name	Required	Default
`style`	No
`video`	No
`task_id`	No
`captions`	No
`language`	No	he
`max_words`	No
`output_path`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 7, 2026

TDQS

A4.4/5.0

Behavior5/5

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

No annotations are provided, but the description carries the full burden and does so well. It reveals default behavior (Scribe transcription), grouping logic (max_words), styling specifics (white bold, black stroke, drop shadow, centered low-third, Hebrew RTL), and customization options (captions, style overrides). This is rich behavioral disclosure.

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 sentences, but each is information-dense. The first sentence states the core function and accepted inputs; the second unpacks default behavior, styling, and alternatives. No redundant phrasing or filler — every clause contributes.

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

Completeness4/5

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

For a complex tool with 7 parameters and an output schema, the description covers the main workflow thoroughly: input sources, default transcription, chunking, styling, and pipeline position. It does not explain output details, but the presence of an output schema reduces that need. Minor gaps like behavior without a video are acceptable given the overall completeness.

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?

Schema description coverage is 0%, so the description must compensate. It explains `captions`, `style`, `max_words`, and mentions `video`/`task_id` as input sources. However, it does not clarify `language` or `output_path`. The description adds value but leaves several parameters undocumented in any form.

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 starts with an explicit verb and resource: 'Burn word-timed captions onto a video.' It clearly differentiates this from sibling tools (e.g., mix_narration, trim_video) by focusing on caption burning with precise details about the default Scribe transcription flow and styling.

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?

The description gives explicit sequencing: 'Run AFTER verify_generated_audio passes and BEFORE stitch_videos.' This tells the agent when in the pipeline to use it. It also notes that passing `captions` bypasses Scribe, providing an alternative path. It lacks a formal 'when not to use' but the contextual guidance is strong.

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