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denisok-ai/magicmaster-mcp

by Various

Audio mastering for AI agents via MCP: LUFS/True Peak targets, Suno/Udio AI-fingerprint removal. Hosted server https://magicmaster.pro/mcp

MCP

denisok-ai/magicmaster-mcp

Added 16 Sept 2026

#audio #mastering #mcp #mcp-server #model-context-protocol #suno #udio

Overview

An MCP server that lets AI agents master audio to LUFS and True Peak targets and remove AI fingerprints from Suno or Udio generated tracks. It uses a hosted endpoint at magicmaster.pro/mcp and includes a Dockerfile for local deployment.

Best for

Best for
AI agent builders who need automated audio mastering and Suno or Udio cleanup.

Use cases

  • Master podcast or music audio to loudness specs
  • Clean AI-fingerprint artifacts from Suno or Udio exports
  • Integrate audio mastering into agent workflows via MCP

How to use

Tools exposed

  • analyze_track
  • clean_ai_trace
  • master_track
  • get_job
  • check_limits
  • create_topup_link

Tested with

Claude Desktop, Claude Code, Cursor, VS Code, ChatGPT

Notes

An MCP server that lets AI agents master audio to LUFS and True Peak targets and remove AI fingerprints from Suno or Udio generated tracks. It uses a hosted endpoint at magicmaster.pro/mcp and includes a Dockerfile for local deployment.

0 stars on GitHub. Last updated 2026-09-08. Licensed MIT.

Use cases

  • Master podcast or music audio to loudness specs
  • Clean AI-fingerprint artifacts from Suno or Udio exports
  • Integrate audio mastering into agent workflows via MCP

Pros

  • Supports standard mastering targets like LUFS and True Peak
  • Offers a hosted server for quick integration
  • Open source Dockerfile enables self-hosting

Cons

  • Repository has no stars or proven track record
  • Core functionality depends on an external hosted service
  • Narrowly focused on AI-generated audio and loudness mastering

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

Pros

  • Supports standard mastering targets like LUFS and True Peak
  • Offers a hosted server for quick integration
  • Open source Dockerfile enables self-hosting

Cons

  • Repository has no stars or proven track record
  • Core functionality depends on an external hosted service
  • Narrowly focused on AI-generated audio and loudness mastering
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