MCP Music Analysis

MCP Music Analysis is an MCP server that enables AI clients like Claude to inspect and process sound recordings using the Python librosa library. It connects Model Context Protocol hosts directly to local audio files, direct web download URLs, and YouTube video links. Designed for sound designers, music researchers, and audio engineers, the server allows large language models to execute complex signal processing tasks on demand without manual file conversion. Users can ask conversational agents to calculate beat tracking, pinpoint onset times, compute Mel-frequency cepstral coefficients, determine track duration, and measure spectral centroid values across different media formats. By converting remote URLs and YouTube links into audio files on the fly, it streamlines audio feature extraction workflows directly within chat-driven desktop environments.

Category: Design, Media & Creative

Tags: analysis, audio, librosa, media, music

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How to install and configure MCP Music Analysis

1. Install the server automatically using Smithery by running npx -y @smithery/cli install @hugohow/mcp-music-analysis --client claude. 2. Alternatively, clone the repository manually with git clone git@github.com:hugohow/mcp-music-analysis.git and navigate to the directory using cd mcp-music-analysis. 3. Create and activate a virtual environment using uv venv and source .venv/bin/activate (or .venv\Scripts\activate on Windows), then run uv pip install -e .. 4. Open your Claude Desktop configuration file (claude_desktop_config.json) and add the server configuration: json { "mcpServers": { "music-analysis": { "command": "uvx", "args": ["-n", "mcp-music-analysis"] } } } 5. Restart Claude Desktop to use the audio analysis tools.

What you can do with MCP Music Analysis

• Detect beat positions and tempo variations across local WAV or MP3 files during music composition. * Measure exact track durations from hosted audio file URLs directly within the chat interface. * Compute Mel-frequency cepstral coefficients (MFCC) to evaluate timbre and sound texture characteristics. * Extract spectral centroid values to inspect frequency brightness across dynamic audio recordings. * Identify note onset timing markers from streaming YouTube audio links for rhythm analysis.

Key facts

• https://github.com/hugohow/mcp-audio-analysis
• Design, Media & Creative, Files, Documents & PDFs
• analysis, audio, librosa, media, music

Part of MCP Servers

What is MCP Music Analysis?

MCP Music Analysis is a Model Context Protocol server that bridges AI clients to the librosa audio library. It provides programmatic tools that allow assistants to analyze local audio files, direct web audio links, and YouTube video audio tracks.

How do I install MCP Music Analysis?

You can install it automatically using the Smithery CLI by running npx -y @smithery/cli install @hugohow/mcp-music-analysis --client claude. Alternatively, clone the repository, install its dependencies using uv pip, and add the music-analysis command to your Claude Desktop configuration file.

Which audio sources are supported by MCP Music Analysis?

The server can process audio files stored locally on your machine, remote audio files accessed via direct web download URLs, and sound extracted from standard YouTube links.

Which MCP clients work with MCP Music Analysis?

MCP Music Analysis is built to work with Claude Desktop on macOS, Windows, and Linux via its local configuration file. It can also be integrated into any MCP-compliant client that supports local stdio command execution.

What features can MCP Music Analysis extract?

The server uses librosa to compute key audio features, including beat tracking, onset times, duration, Mel-frequency cepstral coefficients, and spectral centroid values directly from the supplied media.