AudacityMCP

AI-powered audio editing in Audacity through the Model Context Protocol

Quick Start • Why AudacityMCP? • Pipelines • Tool Reference • Troubleshooting

AudacityMCP connects any MCP-compatible AI assistant to Audacity, giving it full control over audio editing through 131 tools spanning effects, cleanup, mastering, transcription, and more. Talk to your AI assistant and it edits your audio in real-time.

No cloud. No API keys for audio processing. Everything runs locally through Audacity's named pipe interface.

Works With

AudacityMCP works with any AI client that supports the Model Context Protocol:

• Claude Desktop — Anthropic's desktop app
• Claude Code — CLI agent
• Cursor — AI code editor with MCP support
• Any other MCP-compatible client

Quick Start

1. Enable mod-script-pipe in Audacity

Open Audacity → Edit → Preferences → Modules → set mod-script-pipe to Enabled → restart Audacity.

You must restart Audacity after enabling this. The module only loads on startup.

2. Install AudacityMCP

git clone https://github.com/xDarkzx/Audacity-MCP.git
cd Audacity-MCP
pip install -e .

3. Configure your AI client

In Claude Desktop: open Settings → Developer → Edit Config to open claude_desktop_config.json, then add:

{
  "mcpServers": {
    "audacity": {
      "command": "audacity-mcp"
    }
  }
}

Important: Restart Claude Desktop after saving the config. The MCP server won't appear until you restart.

{
  "mcpServers": {
    "audacity": {
      "command": "python",
      "args": ["-m", "server.main"],
      "cwd": "/path/to/Audacity-MCP"
    }
  }
}

4. Start editing

Open Audacity, load some audio, then talk to your AI:

"Clean up this podcast recording"
"Master this track for Spotify, it's EDM"
"Transcribe this and add labels at each sentence"
"Add reverb with a large room, then export as FLAC"

Audacity must be open first. AudacityMCP communicates through Audacity's named pipe — it can't launch Audacity for you.

See the full Installation Guide for detailed setup on all platforms and MCP clients.

Why AudacityMCP?

Without AudacityMCP: You manually navigate menus, tweak effect parameters by ear, apply effects one at a time, look up ACX specs, and repeat until it sounds right.

With AudacityMCP: You describe what you want in plain English and the AI handles the rest — picking the right effects, setting industry-standard parameters, and chaining operations together.

AudacityMCP is especially useful for:

• Podcasters who want consistent, professional sound without audio engineering knowledge
• Musicians who need quick mastering with genre-appropriate settings
• Content creators working with interviews, voiceovers, or field recordings
• Anyone who'd rather describe what they want than click through menus

What Can It Do?

You:  "Clean up this podcast recording"
AI:   Runs auto_cleanup_podcast → HPF 80Hz → noise reduction → compression → safe loudness check

You:  "Master this track for Spotify, it's EDM"
AI:   Runs auto_master_music style=edm → HPF 30Hz → click removal → compression 2.5:1 → bass +2dB → loudness check

You:  "This is a noisy live recording, fix it up"
AI:   Runs auto_cleanup_live → HPF 100Hz → click removal → noise reduction 18dB → compression 5:1

You:  "Transcribe this interview and add labels"
AI:   Runs transcribe_to_labels → faster-whisper transcription → Audacity labels at each timestamp

You:  "Add reverb to the vocals, then export as FLAC"
AI:   select region → reverb effect → export to FLAC

Features

131 Tools Across 11 Categories

Pipelines

AudacityMCP includes 9 one-click pipelines for common audio tasks. Each pipeline is designed to be safe for badly recorded audio — it will never boost your audio dangerously. Pipelines clean up and improve your audio, then you can manually adjust loudness afterward if needed.

How Pipelines Work

1. You tell the AI what you want (e.g. "clean up this podcast")
2. The AI picks the right pipeline and starts it
3. The pipeline runs in the background — you get a job_id back
4. Poll with check_pipeline_status every 15-30 seconds to monitor progress
5. When done, a popup appears in Audacity

Safety rule: Pipelines only reduce peaks if they're too hot. They never boost loudness. If you want to hit a specific LUFS target (e.g. -14 for Spotify), ask the AI to run loudness_normalize after you've checked the results look good.

Pipeline Reference

auto_analyze_audio — Analyze before processing

Measures your audio and recommends the best pipeline. Run this first if you're not sure what to do.

You: "Analyze this audio"
→ Returns: peak level, noise floor, clipping status, recommended pipeline

auto_cleanup_audio — Safe cleanup only

Cleans up noise and artifacts without changing loudness at all. Use when levels are already fine.

You: "Just clean up the noise, don't change the volume"
→ DC offset removal → HPF 80Hz → noise reduction → click removal (optional)

auto_cleanup_podcast — Podcast / voiceover

Professional broadcast processing for spoken word.

You: "Clean up this podcast recording"
→ DC offset → HPF 80Hz → noise reduction 12dB → compression 3:1 → safe loudness check

auto_audiobook_mastering — Audiobook (ACX/Audible)

Targets ACX requirements for audiobook distribution.

You: "Master this for ACX / Audible"
→ DC offset → HPF 80Hz → noise reduction 12dB → compression 2.5:1 → safe loudness check → peak cap -3dB

auto_cleanup_interview — Interview / dialogue

Light touch for conversations — preserves natural dynamics.

You: "Clean up this interview recording"
→ DC offset → HPF 80Hz → noise reduction 8dB → compression 2.5:1 → safe loudness check

auto_cleanup_vocal — Singing / studio vocal

Tuned for vocal recordings with presence EQ for clarity.

You: "Process this vocal recording"
→ DC offset → HPF 100Hz → noise reduction 10dB → compression 3:1 → presence EQ (+3dB treble, -1dB bass) → safe loudness check

auto_cleanup_live — Live / field / noisy recording

Aggressive cleanup for noisy environments. First 0.5s must be ambient noise for profiling.

You: "This is a noisy live recording, clean it up"
→ DC offset → HPF 100Hz → click removal → noise reduction 18dB → compression 5:1 → safe loudness check

auto_master_music — Music mastering

Genre-tuned mastering with 6 presets: edm, hiphop, rock, pop, classical, acoustic.

You: "Master this hip-hop track"
→ HPF 30Hz → click removal → compression 2:1 → bass +3dB treble +1dB → safe loudness check

You: "Master this for a classical album"
→ HPF 30Hz → click removal → compression 1.3:1 (very gentle) → no EQ → safe loudness check

auto_lofi_effect — Creative lo-fi / vintage

Apply a warm, vintage lo-fi sound. Presets: light, medium, heavy.

You: "Give this a lo-fi vibe"
→ HPF → LPF (muffled highs) → bass/treble warmth → compression 2:1 → safe loudness check

After a Pipeline: Adjusting Loudness

Pipelines intentionally leave loudness alone (they only reduce if peaks are clipping). To hit a streaming target:

You: "Now normalize this to -14 LUFS for Spotify"
→ AI uses loudness_normalize tool with lufs_level=-14

You: "Normalize to -16 LUFS for podcast"
→ AI uses loudness_normalize tool with lufs_level=-16

Why not do this automatically? LUFS normalization can boost quiet/badly recorded audio by 10-20 dB, which blows it out. By separating cleanup from loudness, you get to check the results before the final loudness step.

Local Transcription (Experimental)

This feature is experimental and requires separate setup. Everything else works without it.

Powered by faster-whisper — runs entirely offline, your audio never leaves your machine:

• 5 model sizes: tiny, base, small, medium, large-v3
• Transcribe full audio or just a selection
• Export as SRT, VTT, or plain text subtitles
• Auto-add Audacity labels at each spoken segment
• Language detection or specify 99+ languages

Setup required before first use: See Transcription Setup for installation steps.

What's New — v0.1.3

32 new tools (99 → 131), pipeline tuning fixes, and live-tested against Audacity.

• New effects: Reverse, Invert, Repair, AutoDuck, NotchFilter, VocalReduction, AdjustableFade, StudioFadeOut, CrossfadeClips, CrossfadeTracks, ClipFix, SlidingStretch, Tremolo
• New editing: Split (in place), SplitCut, SplitDelete, Disjoin + renamed old split → edit_split_new
• New tracks: StereoToMono, MixAndRenderToNew, MuteAll, UnmuteAll, Resample, AlignEndToEnd, AddLabelTrack
• New selection: CursorToTrackStart/End, CursorToProjectStart/End, SelectCursorToTrackEnd
• New project: EditMetadata, ImportMIDI
• New labels: RegularIntervalLabels
• Pipeline fixes: ACX peak cap -3.0→-3.5dB, live NR 18→12dB, podcast comp 10ms/1s→30ms/200ms, interview release 1s→200ms
• Bug fix: effect_repair now uses long timeout (Audacity shows popup on invalid selection)
• Validation: Added missing range checks on AutoDuck, VocalReduction, SlidingStretch, Resample

Troubleshooting

Connection Issues

Pipeline Issues

Audio Quality Tips

General Issues

Architecture

┌──────────────┐     stdio      ┌──────────────┐   named pipe   ┌──────────────┐
│  MCP Client  │◄──────────────►│ AudacityMCP  │◄──────────────►│   Audacity   │
│(AI assistant)│    (JSON-RPC)  │   FastMCP    │  (commands)    │              │
└──────────────┘                └──────────────┘                └──────────────┘
                                       │
                                       ├── server/main.py          (entry point)
                                       ├── server/audacity_client.py (pipe I/O)
                                       ├── server/tool_registry.py  (auto-loader)
                                       └── server/tools/            (11 modules)

Key Design Decisions

• Named pipes, not TCP — Direct IPC to Audacity's mod-script-pipe. No network exposure, no port conflicts.
• Zero exec/eval — Every operation maps to a static handler with input validation. No arbitrary code execution.
• Cross-platform — Windows uses Win32 API via ctypes, Unix uses standard file I/O.
• Async throughout — All tool handlers are async. Blocking pipe I/O runs in an executor pool with configurable timeouts.
• Safe pipelines — Pipelines measure audio before making loudness decisions. They only reduce, never boost.
• Dynamic tool registration — Drop a module in server/tools/, export a register(mcp) function, and it's automatically discovered.

Project Structure

AudacityMCP/
├── server/
│   ├── main.py                 # FastMCP server entry point
│   ├── audacity_client.py      # Cross-platform named pipe client
│   ├── tool_registry.py        # Auto-discovers and registers tool modules
│   └── tools/
│       ├── analysis_tools.py   # Audio analysis (contrast, spectrum, beats)
│       ├── cleanup_tools.py    # Noise reduction, mastering, 9 pipelines
│       ├── edit_tools.py       # Cut, copy, paste, split, join, trim
│       ├── effects_tools.py    # Reverb, echo, pitch, EQ, filters
│       ├── generate_tools.py   # Tone, noise, chirp, DTMF generation
│       ├── label_tools.py      # Label management
│       ├── project_tools.py    # Project/file operations
│       ├── selection_tools.py  # Selection and cursor control
│       ├── track_tools.py      # Track management
│       ├── transcription_tools.py  # Whisper-based transcription
│       └── transport_tools.py  # Playback and recording control
├── shared/
│   ├── constants.py            # Pipe paths, timeouts, allowed formats
│   ├── error_codes.py          # Typed error codes (pipe/command/validation)
│   └── pipe_protocol.py        # Command formatting and response parsing
├── tests/                      # 60 tests
├── docs/
│   ├── INSTALLATION.md         # Detailed setup guide
│   └── TOOLS.md                # Complete tool reference
└── pyproject.toml

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -x -q

Adding New Tools

1. Create a module in server/tools/ (or add to an existing one)
2. Export a register(mcp: FastMCP) function
3. Define your tools with @mcp.tool() decorators
4. That's it — the tool registry auto-discovers it on startup

# server/tools/my_tools.py
from mcp.server.fastmcp import FastMCP
from shared.error_codes import AudacityMCPError, ErrorCode


def register(mcp: FastMCP):
    from server.main import client

    @mcp.tool()
    async def my_custom_effect(intensity: float = 0.5) -> dict:
        """Apply my custom effect to the selected audio."""
        if not 0 <= intensity <= 1:
            raise AudacityMCPError(ErrorCode.VALUE_OUT_OF_RANGE, "intensity must be 0-1")
        return await client.execute_long("MyEffect", Intensity=intensity)

See CONTRIBUTING.md for full guidelines.

Support

If AudacityMCP has saved you time or helped with your audio projects, consider buying me a coffee:

Your support helps keep this project maintained and free for everyone.

Documentation

• Installation Guide — Detailed setup for Windows, macOS, Linux
• Tool Reference — Complete reference for all 131 tools with parameters and ranges
• Contributing — How to add tools and contribute
• Changelog — Version history and release notes

License

Apache License 2.0 — see LICENSE for details.

Built by Daniel Hodgetts • 𝕏 @daehonz1