audacity-mcp 0.1.4

MCP server for AI-driven audio editing in Audacity via mod-script-pipe

AudacityMCP

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

Quick Start • Installation • Pipelines • Tool Reference • Troubleshooting • Support

---

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.

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

Category	Tools	Highlights
Effects	30	Reverb, echo, pitch shift, tempo change, EQ, phaser, distortion, paulstretch, HPF/LPF, bass & treble, tremolo, wahwah
Cleanup & Mastering	18	Noise reduction, compressor, limiter, 9 one-click pipelines, analysis tool
Editing	13	Cut, copy, paste, split, join, trim, silence, duplicate, undo, redo
Project	12	New, open, save, import/export (WAV, MP3, FLAC, OGG, AIFF)
Track	15	Add mono/stereo, remove, set properties, mix & render, mute/solo, pan, volume
Selection	12	Select all/none/region/tracks, zero crossing, cursor positioning
Transport	7	Play, stop, pause, record, play region, get position
Analysis	6	Contrast, clipping detection, spectrum, beat finder, sound labeling
Generation	5	Tone, noise, chirp, DTMF, rhythm track
Transcription	7	Full/selection transcribe, to labels, to SRT/VTT/TXT, model preload
Labels	6	Add, add at time, get all, edit, import/export

---

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

Preset	HPF	Compression	Bass EQ	Treble EQ
EDM	30 Hz	2.5:1 / 80ms	+2 dB	+1 dB
Hip-Hop	30 Hz	2:1 / 100ms	+3 dB	+1 dB
Rock	40 Hz	2:1 / 100ms	0 dB	+1 dB
Pop	35 Hz	2:1 / 80ms	+1 dB	+1.5 dB
Classical	30 Hz	1.3:1 / 200ms	0 dB	0 dB
Acoustic	30 Hz	1.5:1 / 150ms	0 dB	0 dB

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

Powered by faster-whisper — runs entirely offline:

• 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

---

Quick Start

One-Click Install

Download and run the installer — it installs everything and configures Claude Desktop automatically:

• Windows: Download install.bat → double-click it
• macOS / Linux: curl -fsSL https://raw.githubusercontent.com/xDarkzx/Audacity-MCP/main/install.sh | bash

The installer:

1. Checks Python is installed
2. Installs audacity-mcp from PyPI
3. Creates/updates your Claude Desktop config
4. Tells you exactly what to do next

Manual Install (3 steps)

Click to expand manual installation steps

Prerequisites: Python 3.10+ and Audacity 3.x

Step 1: Enable Audacity's scripting plugin

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

Step 2: Install from PyPI

pip install audacity-mcp

That's it. No git clone needed.

Step 3: Configure your MCP client

Add to your Claude Desktop config (%APPDATA%\Claude\claude_desktop_config.json on Windows, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

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

Restart Claude Desktop.

Other MCP clients (Claude Code, Cursor, etc.) — see the full Installation Guide

Use It

Audacity must be open first. Open it yourself, load your audio, then chat with your AI client.

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

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

---

Troubleshooting

Connection Issues

Problem	Fix
"Pipe not found"	Open Audacity first. Make sure mod-script-pipe is enabled in Edit → Preferences → Modules. Restart Audacity after enabling.
"Pipe timeout"	Audacity is busy. Wait for it to finish — some effects take minutes on long files.
Connection works once then fails	The pipe disconnected (Audacity crash or restart). Just try again — AudacityMCP auto-reconnects.
"Access denied" (Windows)	Audacity and your AI client must run as the same user. Don't mix admin and non-admin.

Pipeline Issues

Problem	Fix
Pipeline blows out / clips the audio	This shouldn't happen anymore — pipelines only reduce peaks, never boost. If it does, undo (Ctrl+Z) and report the issue.
"A pipeline is already running"	Only one pipeline can run at a time. Use check_pipeline_status with your job_id to monitor the current one.
Pipeline finishes but audio is too quiet	That's by design — pipelines don't boost. Ask the AI: "Normalize to -14 LUFS" after checking results.
Noise reduction sounds metallic/warbled	The first 0.5 seconds of your track must be pure silence/room noise for profiling. If it's not, trim to add silence or use auto_cleanup_audio with remove_noise=False.
Pipeline step failed (in warnings)	Individual steps can fail without stopping the pipeline. Check the warnings field in check_pipeline_status for details.

Audio Quality Tips

Want	Do This
Remove background noise	Make sure the first 0.5s of your track is pure room tone (no speech/music). The pipeline uses this to build a noise profile.
Fix clipping	Run auto_analyze_audio first. If it detects clipping, use auto_cleanup_audio before other pipelines.
Hit -14 LUFS for Spotify	Run a cleanup pipeline first, check the results look good, then ask the AI to apply loudness_normalize at -14 LUFS.
Hit -16 LUFS for podcast	Same approach — cleanup first, LUFS second.
ACX audiobook compliance	Use auto_audiobook_mastering. It targets RMS -20 dB with a -3 dB peak cap.
Quick cleanup without changing volume	Use auto_cleanup_audio — it only removes noise and artifacts, no loudness changes.

General Issues

Problem	Fix
"No module named faster_whisper"	Run pip install faster-whisper. Transcription is optional — everything else works without it.
Model download fails	Check internet and retry. Models cache locally after first download.
Pipes missing in /tmp (macOS/Linux)	Check Audacity is running and mod-script-pipe is enabled. Check Audacity's console for errors.

---

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 lunch:

buymeacoffee.com 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
• Contributing — How to add tools and contribute

License

Apache License 2.0 — see LICENSE for details.

Built by Daniel Hodgetts.