MCP Audio Server

@samscarrow

Repository

Basic information

About MCP Audio Server

No overview available yet

#mcp-audio-server#audio-processing#chord-analysis

MCP Audio Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-audio-server": {
      "command": "docker",
      "args": [
        "compose",
        "up",
        "-d"
      ]
    }
  }
}

MCP Audio Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP Audio Server?

MCP Audio Server is a Model Context Protocol server for audio processing and chord analysis. It provides a RESTful API that decodes audio files, detects tempo and key, and performs chord recognition, making it suitable for developers integrating music analysis into MCP workflows.

How to use MCP Audio Server?

The quickest setup is via Docker Compose: clone the repository and run docker compose up -d. Alternatively, install manually with Python 3.10+, FFmpeg, and Poetry, then run poetry run uvicorn mcp_audio_server.main:app --host 0.0.0.0 --port 8000. Send a POST request to /analyze_chords with base64-encoded audio data and format (e.g., wav, mp3, ogg, m4a, flac) to receive chord, key, and tempo results.

Key features of MCP Audio Server

Audio file decoding and normalization with FFmpeg
Tempo detection (BPM)
Key detection
Chord analysis and tracking
RESTful API with structured responses
JSON schema validation for inputs and outputs
Robust error handling with descriptive messages
Resource management with concurrency controls
Caching for performance optimization
Observability with structured logging and metrics

Use cases of MCP Audio Server

Automatically detect chords and key from recorded audio for music transcription.
Analyze tempo and harmonic progression of WAV files for music production.
Integrate real-time chord recognition into a live performance tool using MCP.
Process batches of audio files to generate structured metadata for a music library.

Frequently asked questions

What audio formats are supported?

Supported formats are wav, mp3, ogg, m4a, and flac.

What are the runtime dependencies?

Python 3.10 or higher, FFmpeg, and Poetry (for manual installation). Docker is fully supported.

How do I analyze chords using the API?

Send a POST request to `/analyze_chords` with JSON containing `audio_data` (base64-encoded), `format` (e.g., "wav"), and optional `options.model` ("basic" or "advanced").

Is there a health check endpoint?

Yes, endpoints `/health`, `/ready`, and `/metrics` are available for monitoring and load balancing.

How are errors reported?

Errors return a JSON object with `error_code`, `message`, `details`, and a `correlation_id` for debugging.

Comments

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