ffmpeg-mcp-video-editor

by AbyAbyss

Overview Schema Source Related Servers Discussions Admin

Python

Local

Detect scene cuts

detect_scenes

Identify hard cuts in videos and get scene start/end times to split raw footage into clips.

Instructions

Find hard cuts in a video and report the shots between them.

Useful for chopping raw footage into clips: feed the returned scene start and end times straight into trim. Uses ffmpeg's own scene-change score, so unlike the other phase 4 tools this needs no vision dependency.

Lower the threshold to catch softer cuts, raise it if handheld camera motion is being reported as cuts.

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": {
      "input_path": "<input_path>"
    },
    "name": "detect_scenes"
  }
}

Input Schema

Name	Required	Description
`threshold`	No	Scene-change score to treat as a cut. Lower finds more cuts; 0.3 suits most edited footage, 0.4-0.5 suits noisy handheld video.
`input_path`	Yes	Video file to analyse.
`min_scene_seconds`	No	Discard cuts closer together than this.

Output Schema

Name	Required	Default
`tool`	Yes
`job_id`	Yes
`status`	No	queued
`message`	No	Job queued. Poll job_status for progress, then job_result.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 6, 2026

TDQS

A4.7/5.0

Behavior4/5

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

All annotations are false, offering no positive safety hints, so the description carries the burden. It adds meaningful behavioral context: uses ffmpeg's scene-change score, no vision dependency, threshold effects on cut detection, and the nature of the output (shot start/end times). It does not explicitly state that no files are modified, but the analysis-oriented wording implies it; a clearer read-only statement would earn a 5.

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?

Three concise sentences, front-loaded with the core purpose, then usage context, then parameter guidance. Every sentence earns its place; no fluff or repetition of schema data.

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

Completeness5/5

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

The tool has an output schema (present by context), so return values are covered. Parameter schema is fully documented. The description adds dependency context, integration with sibling trim, and parameter tuning — complete for a detection tool of this complexity.

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

Parameters4/5

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

Schema coverage is 100% and schema descriptions already explain each parameter. The description adds value by giving real-world guidance on how to tune the threshold ('Lower the threshold to catch softer cuts, raise it if handheld camera motion is being reported as cuts') and by connecting the output to another tool, which enhances understanding beyond raw 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 and resource: 'Find hard cuts in a video and report the shots between them.' It distinguishes the tool from siblings by noting it needs no vision dependency unlike other phase 4 tools, and by connecting its output to the trim tool.

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

Usage Guidelines5/5

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

Explicitly says when to use it ('Useful for chopping raw footage into clips'), gives a direct integration path ('feed the returned scene start and end times straight into trim'), and provides practical tuning guidance for threshold and scene length. It also contrasts with other phase 4 tools on dependencies.

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