MCP Audio Server

Name: MCP Audio Server
Author: samscarrow

@samscarrow

代码仓库

关于 MCP Audio Server

暂无概览

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

基本信息

分类

媒体与设计

许可证

MIT license

运行时

python

传输方式

stdio

发布者

samscarrow

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

FAQ from MCP Audio Server

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").