Shotcut MCP

Local video editing through the Model Context Protocol.

Create, edit, validate, preview, and render saved Shotcut projects through your AI assistant. The result stays an editable .mlt timeline, with transactional writes that protect the project as you work.

Demonstration · Quick start · Documentation · Website

Demonstration

A short H.264 export created from a native Shotcut timeline edited through Shotcut MCP.

https://github.com/user-attachments/assets/c70f064f-17e7-403d-9bcf-689a9c616cdf

Quick start

You need Python 3.10+, Shotcut (recommended 26.8.1, bundled MLT 7.41.0), and an MCP client that supports local stdio servers. Shotcut includes the required media tools, codecs, and filters. No additional Python packages are required.

1. Connect your client

The installation guide includes commands for Windows, macOS, and Linux. No pip install is required to run the server. For a source checkout, the guide also shows how to check Python and Shotcut with scripts/shotcut_mcp_server.py --check before connecting MCP.

2. Check readiness

Ask your assistant:

Run shotcut_status and shotcut_doctor. Report tool paths, versions, and failed runtime checks.

Resolve failed runtime checks before editing. See troubleshooting for recovery steps.

3. Make your first edit

Provide the source folder and the destination for the saved project, then describe your edit:

Create a 1920×1080, 30 fps Shotcut project from every video in this folder.
Put narration on A1, add 12-frame crossfades, and save it as documentary.mlt.
Generate a contact sheet so I can review the edit before exporting.

You can request adjustments in ordinary language. See the editing workflow and example prompts for subtitles, quality analysis, effects, and delivery.

Features

• Edit the timeline: work with tracks, clips, transitions, speed changes, titles, and subtitles; discover source media in a folder and apply up to 500 operations in one transaction.