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Kinocut

by KyaniteLabs

Local, guardrailed video editing over MCP with FFmpeg, captions, effects, quality checks, workflows, and provenance receipts.

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Install Kinocut

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "kyanitelabs-kinocut": {
      "command": "uvx",
      "args": [
        "--from",
        "kinocut==2.0.1",
        "kino"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Automated check passed— started and listed 18 tools correctly (Today).

Overview

KyaniteLabs/kinocut MCP server gives AI agents typed tools for local FFmpeg-based video editing, review, quality control, and packaging. It exposes a compact default tool set, expandable profiles, or the complete catalog of 206 tools through MCP, alongside a Python client and kino CLI. Edits are validated before execution, default to new output files, and can produce quality reports and Video Receipts. Reach for it when an agent needs to trim, caption, resize, repurpose, or inspect media without uploading files or requiring API keys.

Use cases

  • Trim interviews into captioned vertical shorts
  • Repurpose podcasts or streams for short-form platforms
  • Run loudness and release-quality checks before export
  • Build resumable multi-step video workflows
  • Create provenance receipts for final renders

Key features

  • 206 MCP tools with default, profile, and full-catalog modes
  • Typed FFmpeg editing with preflight validation
  • Captioning, audio mixing, effects, resizing, and repurposing
  • Quality gates and release checkpoints
  • Resumable JSON workflows and Video Receipts
  • Local execution without required API keys

Tools18

What Kinocut lets your agent do. Expand a tool for its parameters.

Verified live~4.3k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

25/25 checks healthy over the last 91d

What it can do

From the 18 tools this server returned when we ran it; 18 declare their own safety hints.

Can delete or modify data · 14Makes network requests · 5Read-only tools · 4
Network at startup2 connections
Files written at startup10

Observed 2026-11-11 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 6d ago via OSV.dev · kino (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars262Stargazers on the source repository.
Last commitTodayMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed18Callable tools this server registers over MCP.
Directory activity11 viewsConfig copies, upvotes, and views on AllMCPs.

How Kinocut works

What KyaniteLabs/kinocut does

KyaniteLabs/kinocut provides a local video-editing engine through three interfaces: an MCP server for AI agents, a Python client, and the kino command-line tool. Its operations use FFmpeg and cover common editing tasks such as trimming, merging, captioning, resizing, cropping, overlays, speed changes, audio work, frame extraction, and vertical-short creation.

The package is designed around reviewable edits. It normally writes to a new output path rather than replacing the source, validates edit parameters before invoking FFmpeg, and returns structured results that an agent can inspect. Quality checks can identify issues such as black frames, silence, or unsuitable loudness. Video Receipts record the requested operation, execution details, and resulting changes.

How it works

The default MCP configuration exposes 18 entries: eight frequently used tools, eight operation families, and discovery tools for listing and describing operations. Profiles can expose tools for a particular job, while KINOCUT_TOOLS=all makes the full catalog available. Operation families provide access to the broader catalog without requiring every individual tool description to be loaded into the agent context.

The main workflow can combine operations such as retrieving media information, trimming an interview, transcribing it, adding subtitles, changing the aspect ratio, and creating a release checkpoint. Multi-step jobs can also be described as JSON workflows and resumed. Rendering and packaging remain local; publishing media is outside the server's scope.

Setup and configuration

FFmpeg must be installed and available on PATH. The MCP process can be launched with uvx, or the Python package can be installed before starting the kino --mcp command. Python 3.11 or newer is supported. kino doctor checks the local installation and reports which optional features are ready.

MCP clients that read JSON configuration can run the server with a command of uvx, arguments for the kinocut package, and kino --mcp. Claude Code has a corresponding claude mcp add command. Optional capabilities are installed as package extras. The KINOCUT_TOOLS setting selects the default, a named profile, or the complete tool catalog.

A local Claude Desktop extension can be built as an unsigned .mcpb, but it launches the user's own Python environment, still requires Node and FFmpeg, and is not a sandbox.

Tools and capabilities

The hot-path tools include:

  • video_info for media inspection
  • video_trim and video_merge for basic editing
  • video_add_text and video_add_audio for overlays and sound
  • video_extract_frame for still extraction
  • video_shorts for selected-range vertical shorts
  • video_doctor for diagnostics

Operation families extend the catalog across video editing, effects, audio, AI-assisted video work, images, quality control, planning, and platform packaging. Additional workflows support podcast or stream repurposing, damaged-clip rescue, AI-generated-video review, 360-video assembly, product mattes, still-image matching, audio mixing, Hyperframes and Revideo motion graphics, and C2PA provenance on final exports.

Review outputs can include a contact sheet, storyboard, duration and codec information, and a pass/fail quality report. A release checkpoint can be written beside the rendered clip for review outside an MCP client.

Limitations and notes

Kinocut renders existing media rather than generating video or paid images. Caption speech uses local eSpeak NG rather than a neural voice. Optional model features are disabled until enabled, and network behavior can be controlled through the project's vision and network settings.

The server does not publish finished media. Overwriting requires an explicit option, and validation failures return a retryable error with the relevant call. The kinocut_sound program includes a limited S12 join implementation; its benchmark uses synthetic clips rather than a complete episode or human listening approval.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Kinocut

KyaniteLabs/kinocut is an MCP server that connects AI agents to local FFmpeg-based media editing. Its main tools cover video inspection, trimming, merging, captions, audio, frame extraction, shorts creation, diagnostics, quality checks, workflows, and provenance receipts, while keeping source media on the local machine.

Install FFmpeg on your PATH, then launch Kinocut with `uvx` and the `kino --mcp` command or install the Python package and run the same MCP entry point. Use `kino doctor` to check local dependencies and optional feature readiness.

No, the core server runs locally without a required API key. Optional model features are disabled until enabled, and the server does not upload media unless you explicitly configure behavior that requires network access.

Set `KINOCUT_TOOLS=all` to expose the complete catalog, or select a named profile with `KINOCUT_TOOLS=profile:<name>`. The default mode also provides operation families, `list_operations`, and `describe_operation` for discovering additional capabilities.

Not by default. Operations write to a new file, and replacing an existing file requires an explicit `overwrite` option. Inputs are checked before FFmpeg runs, and failed validation returns an error that identifies the retry call.

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