Audio MCP

Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/mshark1-audio-mcp

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What this server can do and what it asks for, from its tool list, install config and code. Checked Oct 25, 2026.

Tools

7 read · 2 write · 0 delete · 6 not yet classified

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Python package

Tools (15)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• audio_analyze

  Measure an audio file (or the start..end section): peak/true-peak/RMS/LUFS loudness, clipping, DC offset, a loudness sparkline over time, silence and noise floor, stereo correlation/width/balance, spectral balance (band energy, brightness, tonal vs noisy, strongest frequencies as notes), pitch, onsets/tempo, start/end clicks and loop-seam quality. Ends with `issues`: plain-language problems ranked by severity. Set pitch/rhythm to false to run faster.

• audio_batch_process

  Apply one operations chain (same format as audio_process) to every file in a folder or list, writing results with the same relative names into output_dir. Refuses to write into the input folder unless overwrite=true. Example: normalize a set of sound effects to the same loudness: [{"op":"trim_silence"}, {"op":"normalize_lufs","lufs":-18,"limit":true}]. subtype: sample format for every output, e.g. PCM_16, PCM_24, FLOAT, or PCM_U8 for 8-bit WAV (retro or low-memory targets: combine with to_mono and resample). Checked before any file is written.

• audio_batch_survey

  Analyze a folder (recursive; filter with a glob `pattern` like "*.wav" or "footstep*") or a list of files, and report how consistent they are: loudness outliers, mixed sample rates/channels/formats, inconsistent leading silence, files with problems. Includes `suggested_fixes` with ready-to-use operations for audio_batch_process, and a per-file table.

• audio_check_delivery

  Check a file against a delivery spec and get a verdict (pass / pass with warnings / fail), one entry per criterion (required, measured, status, note), and `fix`: an ordered audio_process operations list that was simulated on this file (`fix.verified`), plus `cannot_fix` for problems no processing can solve (clipping, a noise floor too high to remove without damaging speech, wrong length). Specs: apple_podcasts, spotify, youtube, ebu_r128, atsc_a85, acx_audiobook, game_sfx (spec="list" describes them). Each result cites its sources and the date they were checked. After a fix that includes denoi…

• audio_check_game_project

  Audit the audio of a Godot 4 project (the folder with project.godot): inventory of every .wav/.ogg/.mp3 with a category (ui, sfx, voice, music, ambience, inferred from folder/file names; override with `categories`, e.g. {"audio/stingers": "music", "*/click_*": "ui"}), import settings from the .import files (music/ambience that doesn't loop, SFX/UI that do, clicky loop seams, never-imported files), loudness consistency per category (one-shots by LUFS momentary max, music/ambience by integrated LUFS; outliers, median, spread), per-file problems (clipping, leading silence, clicky ends, DC offset,…

• audio_compare

  A/B two files: plain-language summary, metric deltas (loudness, peak, brightness, tempo, pitch), per-band level changes, spectral-shape similarity, issues fixed or introduced, and a null test (aligns and subtracts) that tells whether B is identical, gain-changed, lightly or heavily changed. Use after audio_process to verify an edit did what you intended and nothing else.

• audio_events

  Split the audio into distinct sound events (hits, notes, words/syllables, SFX layers) and describe each: start/end, level, attack, tonal vs noisy, brightness, pitch and pitch glide. `as_text` gives one line per event, e.g. "0.52-0.81s | -14dB | tonal | bright | A5 glide -12st | sharp attack". Also reports spacing regularity and the sequence of pitches. The threshold adapts to the background level; set threshold_db (dBFS) to override. split_on_onsets=false keeps continuous sounds (e.g. a sentence) as one event.

• audio_info

  Fast metadata without decoding: format, sample rate, channels, duration. Files read through FFmpeg (M4A/AAC, WMA, MP4/MOV/WebM/MKV video) report decoded_via="ffmpeg", the container as format and the codec as subtype; without FFmpeg they fail with install instructions. If `path` is a directory, lists the audio files inside it (recursively).

• audio_melody

  Transcribe a monophonic line (voice, whistle, solo instrument, synth lead, SFX pitch) into notes with start times, durations, note names and cents deviation, plus a compact text form. Narrow fmin/fmax to the expected range for better accuracy (e.g. bass 40-400, voice 80-1000).

• audio_process

  Apply operations in order and write a NEW file (default: <name>_processed.<ext> next to the input; the input is never modified unless output==path and overwrite=true; M4A/video inputs default to WAV output). Each operation is an object, e.g. [{"op":"highpass","hz":80}, {"op":"normalize_lufs","lufs":-16}, {"op":"fade_out","seconds":0.5}]. Ops: gain(db) normalize_peak(db=-1, true_peak=false) normalize_rms(db=-20) normalize_lufs(lufs=-16, ceiling_db=-1, limit=false, measure=integrated|momentary_max) trim_silence(threshold_db=-60, pad_ms=10) denoise(reduction_db=12, sensitivity=1, noise_start, noi…

• audio_synthesize

  Description is built at runtime.

• audio_timeline

  How the sound changes over time: per-segment RMS, peak and brightness, plus a loudness sparkline. Streams the file, so it works on recordings of any length; use it to find where to zoom in.

• audio_transcribe

  Speech to text (Whisper, runs locally). Returns the text, timed segments, per-word timings with confidence, detected language, speaking rate, long pauses and `uncertain_words` (likely mis-hearings). model: tiny/base/small/medium/large-v3/turbo (bigger = more accurate, slower; first use downloads it). language: ISO code like "en" to skip detection. initial_prompt: names or jargon to help spelling. Use start/end to transcribe part of a long recording.

• audio_view

  Render a picture of the sound. kind: 'both' (waveform above spectrogram), 'waveform' or 'spectrogram'. Spectrogram: time left to right, frequency bottom to top, brighter = louder. Horizontal lines are pitched tones/harmonics, vertical stripes are clicks/transients, diffuse haze is noise. Red dashed lines on the waveform mark 0 dBFS (clipping). Zoom with start/end; pitch_overlay draws the detected f0 in cyan. save_to optionally also writes the PNG to disk.

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218

Last commit

Oct 17, 2026 (12 days ago)

Last release

Oct 17, 2026 (12 days ago)

Last package publish

Oct 17, 2026 (12 days ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.mshark1/audio-mcp

Version

0.4.0

Auth

No auth

Packages

pypi audio-mcp

License

MIT

Repository

github.com/mshark1/audio-mcp

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

Official MCP Registrygithub-graphql