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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

-
security - not tested
F
license - not found
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quality - not tested

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