JuzzyDee

JuzzyDee / audio-analyzer-rs

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MCP server that gives Claude the ability to hear music. Pure Rust audio analysis — spectral, harmonic, rhythm, timbre — returned as structured data.

12
2
100% credibility
Found Mar 12, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

A local tool that lets AI assistants analyze audio files to extract musical attributes like key, tempo, dynamics, loudness, and stereo properties without needing images or uploads.

How It Works

1
🔍 Discover music-listening AI

You hear about a simple tool that lets your AI chat buddy analyze songs like a music expert, spotting key, tempo, and mix issues.

2
📥 Grab the easy download

Pick the ready-made file for your computer (Mac, Windows, or Linux) from the project's page.

3
🚀 Open in your AI app

Double-click the file in Claude Desktop, and it connects automatically—no setup hassle.

4
🎵 AI gains super hearing

Your AI can now 'listen' to any music file on your computer and break it down perfectly.

5
📂 Share a song path

In the chat, tell your AI the exact spot of your track, like your music folder.

6
✨ Get instant music insights

AI reveals the song's key, beat speed, loudness balance, stereo feel, and production tips.

✅ Master your music

You now have pro feedback to improve mixes, check loudness for streaming, or understand any track deeply.

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Star Growth

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AI-Generated Review

What is audio-analyzer-rs?

This Rust-based MCP server equips Claude with the ability to analyze local audio files, delivering structured data on spectral features, harmonic content, rhythm, timbre, and stereo imaging. Point it at an MP3 or WAV via full file paths, and Claude gets key detection, tempo, LUFS loudness, dynamic range, and time-series breakdowns without images or token waste. Like mcp server examples on GitHub, it runs tools such as full_analysis or spectral_features over stdio for Claude Desktop or Code.

Why is it gaining traction?

It stands out with pure Rust speed—full 60-second tracks process in under 2 seconds, no Python or FFmpeg deps—plus pro-grade outputs like EBU R128 LUFS validated against FabFilter and frequency band energy for mix checks. Token-efficient resolutions (low/medium/high) let Claude zoom into sections without context bloat, and one-click .mcpb bundles simplify setup versus manual mcp server docker or Python alternatives. Developers dig the CLI for standalone testing alongside MCP server ai integration.

Who should use this?

Music producers feeding tracks to Claude for mastering feedback on loudness targets or phase issues. AI tool builders extending mcp github copilot vscode with audio analysis. Devs prototyping mcp server tutorial projects or n8n workflows needing rhythm/timbre data without heavy libs.

Verdict

Grab it if you're in the Claude + music niche—solid docs and cross-platform bundles make early adoption easy despite 12 stars and 1.0% credibility score. Still maturing with low test coverage; watch mcp github issues for polish before production.

(198 words)

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