# Audio Analyzer Rs > MCP server that gives Claude the ability to hear music. Pure Rust audio analysis — spectral, harmonic, rhythm, timbre — returned as structured data. - **Type:** MCP server - **Install:** `agentstack add mcp-juzzydee-audio-analyzer-rs` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [JuzzyDee](https://agentstack.voostack.com/s/juzzydee) - **Installs:** 0 - **Category:** [Content & Media](https://agentstack.voostack.com/c/content-and-media) - **Latest version:** 1.0.0 - **License:** MIT - **Upstream author:** [JuzzyDee](https://github.com/JuzzyDee) - **Source:** https://github.com/JuzzyDee/audio-analyzer-rs ## Install ```sh agentstack add mcp-juzzydee-audio-analyzer-rs ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # audio_visualizer_rs **An MCP server that gives Claude the ability to hear music.** Point Claude at any audio file and it can tell you the key, tempo, dynamics, timbre, percussive character, stereo field, structural sections, and how the music evolves over time -- all from raw audio analysis, no images, no guessing, under 1% context window usage. Compare two tracks side-by-side. Detect where the music changes structurally — intro, verse, chorus, bridge — and zoom into the moments that matter. ## What is this? LLMs can see (vision) and read (text), but they can't hear. This project bridges that gap by running real audio analysis -- the same DSP techniques used in music information retrieval research -- and returning structured numerical data that Claude can reason about. It's an [MCP server](https://modelcontextprotocol.io/) that exposes audio analysis as tools Claude can call on demand. Ask Claude to analyze a song and it will decode the audio, run spectral/harmonic/rhythm/percussive analysis, and return the results as compact text. No spectrograms, no images, no wasted tokens. Full analysis of a 60-second track completes in under 2 seconds (including source separation). Pure Rust. No Python, no FFmpeg, no system dependencies. ## Features - **Audio decoding** -- mp3, wav, flac, ogg, aac via Symphonia (pure Rust) - **Spectral analysis** -- centroid (brightness), bandwidth (richness), rolloff, flatness (tonality) - **Frequency band energy** -- RMS energy across 7 standard producer bands (sub-bass through brilliance) for mix diagnosis - **Spectral contrast** -- peak vs valley per band in dB, reveals clarity vs muddiness - **Dynamic range** -- crest factor, loudness range (95th-5th percentile), peak dBFS - **LUFS loudness** -- EBU R128 integrated loudness (ITU-R BS.1770-4 stereo channel summing), true peak (dBTP), loudness range (LRA), streaming platform targets. Validated to 0.0 dB of FabFilter Pro-L 2. - **Stereo field analysis** -- phase correlation (mono compatibility), stereo width (mid/side ratio), L/R balance, mono compatibility score. Per-frame time-series to pinpoint where phase issues occur. - **Temporal features** -- RMS energy (loudness), zero crossing rate (texture) - **Timbre** -- 13 MFCCs (Mel-frequency cepstral coefficients) - **Harmonic analysis** -- chromagram, key detection (Krumhansl-Schmuckler algorithm), tonnetz - **Rhythm analysis** -- tempo estimation, beat tracking, onset detection, tempo stability - **Percussive character** -- harmonic/percussive source separation (HPSS), attack sharpness, onset density - **Section boundary detection** -- multi-feature novelty analysis detects structural changes (energy, spectral, harmonic, texture). Enables a summary→zoom workflow: get the map, then dive into interesting moments - **A/B comparison** -- analyse two tracks side-by-side, get a compact diff table highlighting differences in loudness, dynamics, spectral balance, stereo field, key, and tempo - **Time-series data** -- track how every feature evolves over time at selectable resolution - **Token-efficient** -- downsampled output calibrated to fit comfortably in the context window ## See it in action [Watch a fresh Claude analyse four tracks in one session](https://claude.ai/share/e40ea498-fe3e-4b22-9a70-81edf6637514) — an original piano/brass composition, Bohemian Rhapsody's full structure mapped with section boundaries, an A/B mix comparison with production advice, and a thunderstorm field recording where it triages music metrics as "meaningless" vs "physically real." All four analyses fit in a single context window. ## Installation ### Claude Desktop — one-click install Download the `.mcpb` bundle for your platform from [GitHub Releases](https://github.com/JuzzyDee/audio-analyzer-rs/releases) and open it. Claude Desktop will handle the rest — no config files, no terminal, no setup. | Platform | File | |----------|------| | macOS (Apple Silicon) | `audio-analyzer-darwin-arm64.mcpb` | | macOS (Intel) | `audio-analyzer-darwin-x64.mcpb` | | Windows | `audio-analyzer-win32-x64.mcpb` | | Linux | `audio-analyzer-linux-x64.mcpb` | ### Claude Code — Homebrew (macOS) ```bash brew tap JuzzyDee/tap brew install audio-analyzer claude mcp add --scope user audio-analyzer -- $(which audio-analyzer-mcp) ``` ### Claude Code — manual (all platforms) Download the `mcp-server` binary for your platform from [GitHub Releases](https://github.com/JuzzyDee/audio-analyzer-rs/releases), then: ```bash claude mcp add --scope user audio-analyzer -- /path/to/mcp-server ``` ### Build from source ```bash git clone https://github.com/JuzzyDee/audio-analyzer-rs.git cd audio-analyzer-rs cargo build --release claude mcp add --scope user audio-analyzer -- target/release/mcp-server ``` Restart Claude Desktop. The audio analysis tools will be available in your conversations. **Note**: This is a local MCP server using stdio transport, so it requires Claude Code or Claude Desktop. It does not work with claude.ai in the browser or mobile apps. ## Usage **Important**: This tool analyses files on your local machine. Give Claude the full file path (e.g., `/Users/you/Music/song.mp3`) -- don't try to upload or attach files to the chat. Claude will read the file directly from disk. ### CLI (standalone) ```bash cargo run --bin cli -- /path/to/song.mp3 cargo run --bin cli -- compare /path/to/mix_v1.mp3 /path/to/mix_v2.mp3 ``` ### MCP tools Once configured, Claude can call these tools directly: | Tool | What it does | |------|-------------| | `audio_info` | Basic file info: duration, sample rate, sample count | | `spectral_features` | Brightness, richness, loudness, texture, timbre (MFCCs), frequency band energy, spectral contrast, dynamic range, LUFS loudness, stereo field | | `harmonic_analysis` | Key detection, pitch class distribution, tonnetz | | `rhythm_analysis` | Tempo (BPM), beat positions, tempo stability | | `full_analysis` | Everything above in one call, plus percussive character (HPSS), stereo field, and section boundaries. Recommended workflow: call without resolution first to get summary + section map, then zoom into interesting sections with `start_time`/`end_time` at high resolution | | `compare` | A/B two tracks -- analyses both and returns a compact diff table | ### Example: full_analysis output Here's what `full_analysis` returns for a 60-second jazz trio track: ``` ═══ Full Audio Analysis ═══ File: /music/jazz_trio.mp3 Duration: 60.62 sec | Sample rate: 48000 Hz | Samples: 2909952 Analysis completed in: 2.02s ── Spectral/Temporal Features ── Centroid (brightness): 2812 Hz — moderate Bandwidth (richness): 3933 Hz — complex Rolloff (energy focus): 6489 Hz Flatness (tonality): 0.0824 — strongly tonal RMS Energy (loudness): 0.1160 Zero Crossing Rate: 0.0402 — mixed MFCCs (timbre): [-141.3, 13.7, 0.9, 7.7, -2.5, 3.1, -1.7, 2.3, -0.9, 1.0, -0.5, 0.9, 0.6] ── Frequency Band Energy ── Sub bass (20–60 Hz): 0.007803 Bass (60–250 Hz): 0.013043 Low-mid (250–500 Hz): 0.004906 Mid (500–2k Hz): 0.002165 Upper-mid (2k–4k Hz): 0.000203 Presence (4k–6k Hz): 0.000166 Brilliance(6k–20k Hz): 0.000089 ── Spectral Contrast (peak–valley dB) ── Sub bass (20–60 Hz): 10.4 Bass (60–250 Hz): 20.4 Low-mid (250–500 Hz): 26.0 Mid (500–2k Hz): 30.9 Upper-mid (2k–4k Hz): 19.4 Presence (4k–6k Hz): 16.6 Brilliance(6k–20k Hz): 55.2 ── Harmonic Content ── Estimated key: E minor (confidence: 0.538) Top pitch classes: 1. G 0.627 ███████████████ 2. E 0.561 ██████████████ 3. C 0.512 ████████████ 4. D# 0.495 ████████████ 5. F# 0.487 ████████████ 6. C# 0.459 ███████████ ── Rhythm ── Tempo: 84.0 BPM (confidence: 0.316) Beats detected: 69 Mean tempo: 84.3 BPM | Median: 84.0 BPM Stability: 0.951 (0=free, 1=locked) ── Percussive Character ── Percussive ratio: 0.277 — harmony-dominated Onset density: 7.0/sec — very dense Peak attack sharp: 1.000 ── Dynamic Range ── Peak: -0.44 dBFS Crest factor: 16.2 dB — very dynamic Loudness range: 76.4 dB — very dynamic Quiet sections: -87.5 dBFS | Loud sections: -11.1 dBFS ── Loudness (EBU R128) ── Integrated: -12.1 LUFS True peak: 0.0 dBTP Loudness range: 3.7 LU Spotify (-14): turned DOWN 1.9 dB | Apple (-16): turned DOWN 3.9 dB | YouTube (-14): turned DOWN 1.9 dB ── Stereo Field ── Phase correlation: 0.257 avg, -0.822 min — some phase issues Phase warnings: 20.6% of frames have negative correlation Stereo width: 0.812 avg, 2.747 max — wide Balance: -0.095 — slightly left Mono compatibility: 0.620 avg, 0.117 min — significant mono loss ── Section Boundaries ── Working BPM: 84 (confidence: 0.32) Boundaries: 2 0:18.3s energy+spectral+texture (confidence: 0.85) 0:42.1s energy+harmonic (confidence: 0.67) ``` When you add `resolution: "medium"`, the output also includes a time-series table showing how every feature changes over the track's duration -- letting Claude see the intro build, the dynamic solo section, and the quiet outro. ## Time-series resolution All analysis tools accept an optional `resolution` parameter that controls time-series output: | Preset | Data points/sec | Use case | |--------|----------------|----------| | `"low"` | ~0.5/sec | Broad overview, equivalent to what you'd eyeball from a spectrogram image | | `"medium"` | ~1/sec | Good default for most analysis tasks | | `"high"` | ~4/sec | Detailed view for short passages or zooming in on transitions | You can also pass a numeric string (e.g., `"20"`) for custom rates. Without `resolution`, tools return summary statistics only (averages across the whole track). With it, you get a compact TSV table showing how features evolve over time -- centroid, RMS, dynamic range, chroma, onset strength, percussive ratio, band energy, spectral contrast, and more, all aligned to the same time axis. The presets are calibrated for token efficiency. A 3-minute track at `"medium"` resolution produces roughly 180 rows of data -- enough to track musical structure without blowing up the context window. ## Architecture ``` audio file | +---> load_audio() -- Symphonia decodes to mono f32 samples | | | v | compute_spectrogram() -- STFT via rustfft, time-frequency matrix | | | +---> spectral.rs -- centroid, bandwidth, rolloff, flatness, MFCCs, band energy, contrast | +---> temporal.rs -- RMS energy, zero crossing rate, dynamic range | +---> harmonic.rs -- chromagram, key detection, tonnetz | +---> rhythm.rs -- onset detection, tempo, beat tracking | +---> percussive.rs -- HPSS (source separation), attack sharpness, onset density | +---> sections.rs -- section boundary detection (multi-feature novelty) | +---> load_audio_stereo() -- preserves L/R channels | +---> stereo.rs -- phase correlation, width, balance, mono compatibility +---> temporal.rs -- LUFS loudness (ITU-R BS.1770-4 stereo channel summing) | v downsample.rs -- bin-average to target resolution, format as TSV ``` Two binaries share the same analysis library: - **`cli`** (`src/main.rs`) -- runs all analyses and prints results - **`mcp-server`** (`src/mcp_server.rs`) -- exposes tools over stdio JSON-RPC via rmcp Key dependencies: [symphonia](https://github.com/pdeljanov/Symphonia) (audio decoding), [rustfft](https://github.com/ejmahler/RustFFT) (FFT), [rmcp](https://github.com/anthropics/rmcp) (MCP SDK). ## Changelog See [CHANGELOG.md](CHANGELOG.md) for version history. ## License MIT License. See [LICENSE](LICENSE) for details. ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [JuzzyDee](https://github.com/JuzzyDee) - **Source:** [JuzzyDee/audio-analyzer-rs](https://github.com/JuzzyDee/audio-analyzer-rs) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v1.0.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **1.0.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-juzzydee-audio-analyzer-rs - Seller: https://agentstack.voostack.com/s/juzzydee - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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