Provides pass/fail delivery checks against Apple Podcasts specifications, including measured criteria, cited sources, and suggested audio processing fixes.
Provides pass/fail delivery checks against Spotify specifications, including measured criteria, cited sources, and suggested audio processing fixes.
Provides pass/fail delivery checks against YouTube specifications, including measured criteria, cited sources, and suggested audio processing fixes.
Audio perception for AI agents. Language models can't hear. This MCP server turns sound into things they can reason about: measurements, plain-language problem reports, spectrogram images, note lists, event timelines, transcripts and before/after diffs. It can also edit and synthesize audio, so an agent can work in a loop: make or fix a sound → measure it → look at it → verify the change.
Everything runs locally. No GPU, no API keys. Core dependencies: numpy, scipy, soundfile, matplotlib.
Real output (JSON abbreviated) from the bundled demo, scripts/demo.py:
> audio_analyze voice_raw.wav # speech recorded with mains hum and hiss
levels: lufs_integrated -32.16, true_peak_dbtp -11.76, clipped_samples 0
issues: [warning] Mains hum at 60 Hz (peaks at 60, 180 Hz). Fix: notch each with
{"op":"eq","hz":60,"db":-30,"q":30}, {"op":"eq","hz":180,"db":-30,"q":30};
for voice, highpass at ~80-100 Hz also removes the fundamental.
[info] Noise floor around -33.4 dBFS in the quietest parts; hiss or background noise may be audible.
> audio_events sfx.wav # a zap, a noise hit, another hit, a rising chirp
0.09-0.36s | -4dB | tonal | balanced | glide G#6->B3 (-33st) | sharp attack
0.59-0.69s | -16dB | noisy | very bright/harsh | sharp attack
...
> audio_melody melody.wav # "Mary Had a Little Lamb", transcribed blind
E4(0.23) D4(0.24) C4(0.23) D4(0.24) E4(0.25) E4(0.25) E4(0.5) D4(0.24) D4(0.26) D4(0.49) E4(0.25) G4(0.26) G4(0.47)Tool | What it gives the agent |
| Peak, true peak, RMS, LUFS loudness, clipping, DC offset, a loudness sparkline, silence and noise floor, stereo correlation/width/balance, band energy, brightness, tonal vs. noisy, strongest frequencies as note names, pitch, onsets, tempo, edge clicks, loop-seam quality, and an |
| Splits audio into distinct sounds and describes each one: timing, level, attack, tonal/noisy, brightness, pitch or glide |
| PNG waveform and/or spectrogram, with an optional pitch overlay. Every column covers its full time span, so short clicks aren't skipped on long files |
| Level and brightness over time. Streams the file, so it works on recordings of any length |
| Single-line transcription: notes, timings, cents, compact text |
| Speech to text with local Whisper: timed words, language, speaking rate, long pauses and uncertain words (optional extra) |
| A/B summary, metric deltas, per-band dB changes, issues fixed or introduced, and a null test (identical? only a gain change? lightly changed?) |
| An ordered chain of edits written to a new file: gain, peak/LUFS normalize, true-peak limiter, compressor, noise reduction, RMS normalize, trim, fades, crop, pad, high/low-pass, peaking EQ, shelves, DC removal, polarity, mono/stereo, resample, reverse, seamless-loop crossfade, mix, append |
| Audits a folder for consistency (loudness outliers, mixed sample rates/channels/formats, uneven leading silence) and returns ready-to-run fixes |
| Applies one edit chain to many files and writes them to an output folder |
| Declarative synth: band-limited oscillators with phase control, noise colours (pink/brown loop seamlessly), sweeps, ADSR, vibrato, tremolo, filters, pan, note sequences, plus exact test signals (impulse, DC, silence) |
| Metadata for a file, or a listing of the audio files in a folder |
| Pass/fail against a delivery spec (Apple Podcasts, Spotify, YouTube, EBU R 128, ATSC A/85, ACX audiobooks, game SFX) with cited sources and a fix that has already been tried on your file |
| Audits a Godot 4 project's audio: loudness consistency per category, loop settings and clicky seams, defects, and broken or unused |
Reads WAV, FLAC, OGG/Vorbis, Opus, MP3, AIFF and CAF natively (via libsndfile). With FFmpeg
installed it also reads M4A/AAC (phones, Zoom, voice memos), WMA and the audio of video files (MP4, MOV, WebM, MKV):
winget install Gyan.FFmpeg on Windows, brew install ffmpeg on macOS, sudo apt install ffmpeg on Debian/Ubuntu,
or set AUDIO_MCP_FFMPEG to the binary. Each file is decoded once per session. Edited files are written as
WAV/FLAC/OGG/AIFF/MP3.
audio_check_delivery tells you whether a file meets a platform's spec and how to fix it if it doesn't. Every
criterion shows what's required, what was measured and whether it passes. The suggested fix is an ordered
audio_process chain (noise handling before gain, true-peak limiting last) that has already been simulated on
your file, and the tool says plainly when something can't be fixed automatically, or when heavy background noise
needs a person's judgment. Spec numbers cite their official sources and the date they were checked.
audio_check_game_project(project_dir) audits every .wav/.ogg/.mp3 in a Godot 4 project. It infers categories
(UI, SFX, voice, music, ambience) from folder and file names, or takes your own mapping. It reads the .import
settings to find music that won't loop, sound effects that loop forever, and loop seams that click. It compares
loudness within each category (short sounds by their loudest moment, music by integrated LUFS) and reports
outliers with fixes you can run directly. It also scans scenes and scripts for broken, wrongly cased or unused
res:// audio references. It never modifies the project.
Related MCP server: sox-mcp
pip install audio-mcp # or: uv tool install audio-mcp
pip install "audio-mcp[speech]" # adds audio_transcribe (faster-whisper; downloads a ~145 MB model on first use)Claude Code
claude mcp add audio -- uvx audio-mcp
# or, if installed with pip:
claude mcp add audio -- audio-mcpClaude Desktop, Cursor, and other clients (JSON config):
{
"mcpServers": {
"audio": { "command": "uvx", "args": ["audio-mcp"] }
}
}Then ask things like "check the sound effects in ./sfx for problems", "clean up interview.wav for a podcast" or "design a short UI confirm sound and check it isn't harsh".
By default the server can read and write any file your user account can. To confine it, set
AUDIO_MCP_ALLOWED_DIRS to a list of directories (separated by ; on Windows, : elsewhere):
{ "command": "uvx", "args": ["audio-mcp"], "env": { "AUDIO_MCP_ALLOWED_DIRS": "/home/me/audio" } }Paths are checked after resolving .. and symlinks. Other settings: AUDIO_MCP_MAX_SECONDS (largest section loaded
into memory per call, default 1800), AUDIO_MCP_WHISPER_MODEL (default base), AUDIO_MCP_ROOT (base for
relative paths).
The tests mostly use synthetic signals whose correct answer is known by construction, so they check correctness rather than just "no crash":
Loudness: our streaming BS.1770 meter matches pyloudnorm's exact-filter mode within 0.01 LU from 8 to 96 kHz, and reads the EBU reference tone (997 Hz, 0 dBFS) at −3.01 LUFS.
Limiter: a +15 dBTP input comes out at exactly −1.0 dBTP with no samples touched by the safety clip.
Pitch and melody: sines, chirps and synthesized melodies are recovered note for note, including repeated notes; pitch is within 1 cent from 82 Hz to 1760 Hz.
Tempo, onsets, events, silence, stereo phase, hum, clipping, alignment offsets: each is checked against a constructed signal.
A contract test runs every tool on every content type (silence, DC, noise, full-scale square), length
(1 sample to 3 s), format (16/24/float WAV, FLAC, Vorbis), channel count (1, 2, 6) and sample rate (8–96 kHz).
Each call must either succeed or fail with an AudioError that explains itself.
Memory: analysis is chunked. A 20-minute stereo 48 kHz file analyzes in about 19 s with about 1.2 GB peak memory, 0.7 GB of which is the audio itself.
Never destructive by default. Edits write <name>_processed.wav; overwriting needs overwrite=true, and
batch output can't target the input folder.
Errors say what to do next. An unknown op lists every op with its parameters; an undecodable file names the supported formats and how to install FFmpeg for the others. Unexpected errors are reported as bugs instead of being hidden.
Honest about uncertainty. Chords set polyphony_likely instead of reporting a confident wrong pitch, weak
tempos carry a low-confidence note, Whisper's uncertain words are listed, and LUFS is null for clips under
0.4 s.
Compact output. Rounded numbers, sparklines and one-line event summaries keep responses small enough for an agent's context.
Pitch, melody and per-event pitch assume one note at a time. Chords and full mixes are flagged, not transcribed.
Denoise is a spectral gate for steady noise (hiss, hum, fans, room tone). It doesn't remove clicks, music or
other voices. Too much sensitivity dulls quiet consonants, so verify speech edits with audio_transcribe.
Tempo is a classic autocorrelation estimate; half- and double-tempo errors happen on complex music.
The tools describe sound, but they don't judge taste ("is this satisfying?").
audio-mcp-assets is a public-domain pack of test and calibration audio (tones, sweeps, calibrated noise, loudness, stereo and phase tests, impulses, problem files) that an AI agent built using only these tools. Every file is verified against known ground truth, twice, including independently of this project.
Improvements from people and AI agents are welcome. See CONTRIBUTING.md; AI coding agents should also read AGENTS.md. The bar for a feature is that it does one thing well, with a test against known ground truth.
Audio for your agent: transcribe, speak, translate, summarise, plus sound effects and music.
Audio and video tools for your AI assistant: remove noise, enhance voices, transcribe, and more.
Audio AI tools: transcription with speakers, stems, noise removal, MIDI, speech, voices and music.
49 audio tools: trim, convert, normalize, remove vocals, split stems, transcribe, and more.