audiolab

MCP serverFiles & storage

Lets your agent analyze audio loudness, peak levels, and voice quality in local files or online.

Unavailable. This server has no hosted endpoint yet, so ahel can't serve it.

Add to setup to save this item as a reference. ahel cannot run it, and signing in will not install it.

About this server

Loudness (EBU R128 / BS.1770-4), true-peak and voice-quality analysis for local files and URLs.

Getting started

  1. Save this item in Your setup as a reference.
  2. Read the source or reference documentation for its setup requirements. Saving it here does not connect it to your AI.
  3. Check this page for availability before trying to install it through ahel.

From the project's README

As published by audio-launch/audiolab-mcp-server in README.md.

@audiolabtools/mcp-server

MCP (Model Context Protocol) server that gives any MCP-capable AI (Claude Desktop, Claude Code, Cursor, and others) ten audio-analysis tools, backed by the hosted AudioLab API. It is a thin HTTP client: no local audio engine, no ffmpeg, nothing to compile. It can analyse a public URL or a local file on your machine.

Install

Point your MCP client at the package via npx (nothing to install globally):

{
  "mcpServers": {
    "audiolab": {
      "command": "npx",
      "args": ["-y", "@audiolabtools/mcp-server"],
      "env": { "AUDIOLAB_API_KEY": "al_live_yourkey" }
    }
  }
}

Get a key: sign in at https://audiolab.tools/account and generate one (free tier available).

Requirements

  • Node ≥ 18: uses the built-in global fetch + AbortSignal.timeout.
  • An AUDIOLAB_API_KEY. No ffmpeg, no native dependencies.

Tools

Every tool takes one audio source: a public url or a local path:

  • { url: "https://…" }: a public https URL the API fetches server-side.
  • { path: "./mix.wav" }: a file on the machine running this server. Files up to 4 MB are sent inline; larger files (up to 50 MB) upload over a one-shot signed URL, are analysed, and are then deleted. (Local path works only in this stdio server, not the remote /mcp endpoint.)
ToolReturns
analyze_loudnessIntegrated LUFS (EBU R128 / BS.1770-4), true-peak (dBTP), LRA, crest factor, stereo correlation, mono compatibility, tonal balance
check_targetPass/fail vs a delivery target (spotify / apple-music / youtube / tidal / amazon-music / podcast / ebu-broadcast / atsc-broadcast, or target:"custom" + lufs+tp), with per-metric deltas and an ffmpeg loudnorm fix command
analyze_timeseriesShort-term LUFS over time + downsampled waveform peaks (waveformPoints?)
get_spectrumFFT magnitude data + 7-band energies
analyze_voiceVoice QA: speech/silence ratio, speaking rate, SNR, noise floor, room echo, sibilance & clipping risk
get_speech_segmentsVoiced regions with start/end + per-segment RMS (auto-trim, chapters)
index_signalContent-type guess, tags, clipping/silence regions, brightness & dynamics buckets
analyze_profileOne named question, voice, master, provenance, dataset, environment, broadcast or loop, answered with only the lenses it needs. These seven need a paid plan; the free tier gets the basic profile, keyed by stable lens id. Add series:true for the curves, per-block lanes and per-phrase values. Carries a note when the profile’s voice lenses land on non-speech material. Full catalogue: https://audiolab.tools/lenses
compare_loudnessA/B on two sources (urlA/pathA + urlB/pathB), returns both results
analyze_batchOne route over up to 20 sources in a single call (urls and/or paths), per-item ok/data/error. For folder QA, library indexing, or checking a whole release against a target. Each item meters as one call

Example asks to your AI:

  • “Analyze the loudness of https://example.com/track.wav” → analyze_loudness with url
  • “Is this take usable?” → analyze_profile with profile:"voice"
  • “Has this file been re-encoded, and is it all one source?” → analyze_profile with profile:"provenance"
  • “Run loudness on ./master.wav” → analyze_loudness with path
  • “Does ./mix.mp3 pass Spotify?” → check_target with path + target:"spotify"

Configuration (env)

VarDefaultPurpose
AUDIOLAB_API_KEY– (required)Your API key.
AUDIOLAB_API_BASEhttps://audiolab.tools/v1Override the API base (must be https://).
AUDIOLAB_TIMEOUT_MS330000Per-request timeout in milliseconds (long files and batches stream server-side and can legitimately take minutes).

Privacy

Analysis happens on the AudioLab API, so the audio does reach audiolab.tools: a url is fetched server-side, and a local path is sent to the API (small files inline; larger files via a private one-shot signed upload that is deleted right after analysis). The API returns numbers only and does not retain your audio (see https://audiolab.tools/privacy). This package has no telemetry and writes nothing to disk. If audio must never leave the machine, don't use a hosted analyser.

Limits

  • Local files: up to 50 MB (host bigger ones at a public URL).
  • Duration: loudness routes (analyze_loudness, check_target, analyze_timeseries, get_spectrum) handle long files (podcast episodes, full sets, up to ~3 h) via server-side streaming; voice/signal routes are limited to ~7 minutes.
  • One file per call (agents loop for many); one-shot (no streaming/realtime).
  • Rate and monthly limits are enforced by the API, per key.

Smoke test

node hosted-server.mjs --selftest   # verifies the 10 tools + guards; no network

License

MIT © Nathan Renting

Signals

Last commit
Sep 2026
Weekly downloads
215
Weekly_downloads
66 weekly_downloads
Advanced
Delivery
audiolab MCP server → your ahel connector (mcp.ahel.ai) → your AI.
Item type
mcp-server
Key
tools-audiolab-audiolab
Source
github.com/audio-launch/audiolab-mcp-server