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MCP Audio Server

@samscarrow

リポジトリ

基本情報

カテゴリ

メディアとデザイン

ライセンス

MIT license

トランスポート

stdio

作者

samscarrow

MCP Audio Server について

概要はまだありません

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

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

MCP Audio Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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.

よくある質問

1

What audio formats are supported?

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

2

What are the runtime dependencies?

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

3

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").

4

Is there a health check endpoint?

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

5

How are errors reported?

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

コメント

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