Audio Analysis MCP Server

An MCP server that gives Claude Code the ability to analyze audio files without ears. Provides numerical fingerprints, visual spectrograms, pitch tracking, and more - all through a single, token-efficient tool.

Overview

This server exposes one tool (audio_analyze) with multiple operations, keeping the MCP schema small and token usage minimal. Visual outputs (spectrograms, waveforms, etc.) are saved to disk and paths returned - Claude can then read the images separately if needed.

Installation

cd ~/projects/audio-analysis-mcp
~/.local/bin/uv sync

If you don't have uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

Configuration

Add to your project's .mcp.json:

{
  "mcpServers": {
    "audio-analysis": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/audio-analysis-mcp",
        "python",
        "-m",
        "audio_analysis_mcp.server"
      ],
      "env": {
        "AUDIO_ANALYSIS_OUTPUT_DIR": "./audio-analysis-output"
      }
    }
  }
}

Or add to ~/.claude.json to make it available globally.

Operations

Single tool: audio_analyze(path, op, [path2])

Numerical Analysis

Op	Description	Output
fingerprint	RMS, peak, spectral stats	{rms, peak, zcr, centroid, bandwidth, rolloff, duration}
formants	Estimated F1-F4 frequencies	{f1, f2, f3, f4}
compare	Compare two files numerically	{identical, max_diff, rms_diff, pct_change}
diff	Sample-level difference	{identical, max_diff, mean_diff}
onsets	Detect transients/attacks	{count, times}
batch	Fingerprint multiple files	{results: [...]}

Visual Analysis

Op	Description	Output
spectrogram	Mel spectrogram image	{output_path}
waveform	Amplitude over time	{output_path}
waterfall	3D spectral surface	{output_path}
pitch	F0 tracking plot + stats	{f0_mean, f0_min, f0_max, output_path}

Output Directory

Images are saved to the directory specified by AUDIO_ANALYSIS_OUTPUT_DIR env var. Defaults to ~/.audio-analysis-mcp if not set.

Claude Code Skill & Slash Command

This project includes a Claude Code skill and slash command for structured audio comparison workflows.

Installing the Skill

Copy the skill to your Claude Code skills directory:

cp -r .claude/skills/analyze-audio-iterations ~/.claude/skills/

This enables automatic detection when you're comparing audio files, with structured workflows for:

• Running all 7 analysis types in parallel
• Building metrics comparison tables
• Tracking improvements across versions
• Pattern detection (oscillation, trade-offs, plateaus)

Installing the Slash Command

Copy the slash command to your Claude Code commands directory:

cp .claude/commands/analyze-audio.md ~/.claude/commands/

Then use it with:

/analyze-audio /path/to/reference.wav /path/to/synthesized.wav [version-context]

Quick Install (Both)

cp -r .claude/skills/analyze-audio-iterations ~/.claude/skills/ && \
cp .claude/commands/analyze-audio.md ~/.claude/commands/

Dependencies

• mcp - Official MCP Python SDK
• librosa - Audio analysis
• matplotlib - Visualizations
• numpy, scipy - Numerical operations