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1000ri-jp/atsurae

by Various

MCP Server for AI-powered video editing — Let Claude, GPT, or any AI agent edit videos through natural language

1

MCP

1000ri-jp/atsurae

Added 1 June 2026

#ai #claude #ffmpeg #mcp #model-context-protocol #video-editing

Overview

Atsurae is an MCP server that exposes video editing capabilities to AI agents via natural language. It translates commands from models like Claude or GPT into programmatic video edits using Python.

Best for

Best for
Developers prototyping AI-assisted video editing workflows

Use cases

  • Automating repetitive video trimming and concatenation tasks
  • Enabling non-technical users to edit videos through chat interfaces
  • Integrating AI-driven video editing into existing workflows or pipelines

Notes

Atsurae is an MCP server that exposes video editing capabilities to AI agents via natural language. It translates commands from models like Claude or GPT into programmatic video edits using Python.

1 stars on GitHub. Last updated 2026-02-16. Licensed MIT.

Use cases

  • Automating repetitive video trimming and concatenation tasks
  • Enabling non-technical users to edit videos through chat interfaces
  • Integrating AI-driven video editing into existing workflows or pipelines

Pros

  • Leverages familiar MCP protocol for easy agent integration
  • Written in Python, accessible to a wide developer audience
  • Enables natural language control of video editing without manual scripting

Cons

  • Very early stage with only 1 GitHub star and minimal community adoption
  • Limited documentation and unknown reliability for complex edits
  • Dependent on external AI models for accurate command interpretation

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Leverages familiar MCP protocol for easy agent integration
  • Written in Python, accessible to a wide developer audience
  • Enables natural language control of video editing without manual scripting

Cons

  • Very early stage with only 1 GitHub star and minimal community adoption
  • Limited documentation and unknown reliability for complex edits
  • Dependent on external AI models for accurate command interpretation