Ableton For Ai MCP Server

@peterkolbe

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peterkolbe

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Peter Kolbe

Ableton For Ai MCP Server について

Bridge between Ableton Live and AI models. Inspect tracks, analyze audio (LUFS, spectrograms), and control mixing parameters in real-time via MCP.

#mcp#ableton#ableton-live#music-production#audio-analysis#daw #mixing #osc #ai#spectrograms#stems#music

Ableton For Ai MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "ableton-for-ai": {
      "command": "uvx",
      "args": [
        "ableton-for-ai"
      ],
      "env": {
        "STEMS_SOURCE_DIR": "/path/to/your/stems"
      }
    }
  }
}

Ableton For Ai MCP Server のツール

List resources available on the server.

Read a resource available on the server.

DEEP AUDIO ANALYSIS: Triggers the generation of summaries and spectrograms for all tracks. This is a heavy operation that: 1. Generates '.summary.json' (Frames, Transients, LUFS) for AI consumption. 2. Generates '.spectrogram.webp' (Log-frequency visualizations). 3. Generates '.analysis.json' (Full resolution data for internal use). ACTION: Use this when you need fresh audio data (e.g., after the user recorded new parts or changed audio effects that significantly alter the sound).

SESSION DISCOVERY: Returns a high-level overview of the current Live set. Includes: - Tempo and Locators (Song structure). - Simplified Track list with mixer states (Index, Name, Volume, Panning, Mute, Solo). Use this for initial navigation and to identify track indices for further 'get_track' calls.

TRACK INSPECTION: Returns comprehensive data for a single track. Includes: - Mixer state (Volume, Panning, etc.). - Complete Device Chain. - All Device Parameters (including UI-readable strings like "-12.0 dB" or "1500 Hz"). Use this when you need to understand exactly how a specific track is processed or which parameters are available for manipulation.

BULK TRACK INSPECTION: Query full data for multiple tracks in a single call. Parameters: - index_min: Starting track index. - index_max: Ending track index. Returns an array of track objects, identical in structure to 'get_track'. Highly efficient for analyzing groups of tracks (e.g., all drum tracks).

REMOTE CONTROL: Sets a specific device parameter. Parameters: - track_index: The index of the track. - device_index: The index of the device in the chain. - parameter_index: The index of the parameter to change. - value: The new normalized value (0.0 to 1.0). Note: For UI-readable values (like dB or Hz), check 'get_track' output first, but always send the normalized 0.0-1.0 value for the actual change.

BULK REMOTE CONTROL: Sets multiple parameters for a device at once. Parameters: - values: A list of floats representing the new values for ALL parameters of the device. Recommended for loading 'presets' or making simultaneous multi-parameter adjustments.

MIXER CONTROL: Sets the volume of a track. - value: 0.0 to 1.0 (corresponds to Ableton's internal fader scaling).

MIXER CONTROL: Sets the panning of a track. - value: -1.0 (Left) to 1.0 (Right), 0.0 is Center.

Toggles the mute status of a track (True=Muted, False=Active).

Toggles the solo status of a track (True=Solo, False=Normal).

概要

What is Ableton For Ai?

Ableton For Ai is an MCP server that bridges Ableton Live and AI models, enabling real-time inspection, analysis, and modification of music projects. It makes a running Ableton session hearable and visible to AI via OSC.

How to use Ableton For Ai?

Install the server with Python 3.11+ and AbletonOSC as a Control Surface in Ableton Live 11 or 12. Invoke MCP tools such as get_overview, analyze_stems, set_track_volume, and set_device_parameter to interact with the session.

Key features of Ableton For Ai

Real-time project inspection — tempo, tracks, mixer state, device chains
Deep audio analysis — LUFS, Peak, RMS, transient detection, spectrograms
Remote mixing control — volume, pan, mute, solo, any device parameter
Sidechain detection — identifies active external sidechain inputs
Bulk device parameter changes for preset loading

Use cases of Ableton For Ai

Analyze a mix and suggest EQ adjustments to reduce frequency clashing
Set kick volume to -6dB and reduce reverb send on vocals
Show spectrogram of the bass track and identify problematic resonances
Get an overview of all track levels and suggest better gain staging

よくある質問

What software is required to run Ableton For Ai?

Ableton Live 11 or 12 with AbletonOSC installed as a Control Surface, and Python 3.11 or newer.

What can the server read from an Ableton Live session?

It reads tempo, locators, all tracks with mixer state, device chains, and all plugin parameters in real time.

Can the server modify audio parameters during playback?

Yes — it can adjust volume (0.0–1.0), panning (-1.0 to 1.0), mute/solo, and any device parameter by normalized value.

What kind of audio analysis does it provide?

It generates LUFS, Peak, RMS, transient detection, and log-frequency spectrograms (WebP) for individual stems.

How does Ableton For Ai communicate with Ableton Live?

It uses the Open Sound Control (OSC) protocol via the AbletonOSC extension.

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