Loudness (EBU R128 / BS.1770-4), true-peak and voice-quality analysis for AI agents. Reads local files as well as URLs.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
git clone https://github.com/Audio-Launch/audiolab-mcp-server{
"mcpServers": {
"audiolab": {
"command": "node",
"args": ["/path/to/audiolab-mcp-server/dist/index.js"]
}
}
}MCP Servers overview
<!-- Built by scripts/build-public-mcp-repo.mjs in the AudioLab monorepo.
Edit mcp/README.md there, not this copy. -->
# @audiolabtools/mcp-server
MCP ([Model Context Protocol](https://modelcontextprotocol.io)) 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):
```json
{
"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.)*
| Tool | Returns |
|---|---|
| `analyze_loudness` | Integrated LUFS (EBU R128 / BS.1770-4), true-peak (dBTP), LRA, crest factor, stereo correlation, mono compatibility, tonal balance |
| `check_target` | Pass/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_timeseries` | Short-term LUFS over time + downsampled waveform peaks (`waveformPoints?`) |
| `get_spectrum` | FFT magnitude data + 7-band energies |
| `analyze_voice` | Voice QA: speech/silence ratio, speaking rate, SNR, noise floor, room echo, sibilance & clipping risk |
| `get_speech_segments` | Voiced regions with start/end + per-segment RMS (auto-trim, chapters) |
| `index_signal` | Content-type guess, tags, clipping/silence regions, brightness & dynamics buckets |
| `analyze_profile` | One 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_loudness` | A/B on two sources (`urlA`/`pathA` + `urlB`/`pathB`), returns both results |
| `analyze_batch` | One 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)
| Var | Default | Purpose |
|---|---|---|
| `AUDIOLAB_API_KEY` | – (required) | Your API key. |
| `AUDIOLAB_API_BASE` | `https://audiolab.tools/v1` | Override the API base (must be `https://`). |
| `AUDIOLAB_TIMEOUT_MS` | `330000` | Per-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
```sh
node hosted-server.mjs --selftest # verifies the 10 tools + guards; no network
```
## License
MIT © Nathan Renting
What people ask about audiolab-mcp-server
What is Audio-Launch/audiolab-mcp-server?
+
Audio-Launch/audiolab-mcp-server is mcp servers for the Claude AI ecosystem. Loudness (EBU R128 / BS.1770-4), true-peak and voice-quality analysis for AI agents. Reads local files as well as URLs. It has 0 GitHub stars and its last recorded update is dated 2026-09-20.
How do I install audiolab-mcp-server?
+
You can install audiolab-mcp-server by cloning the repository (https://github.com/Audio-Launch/audiolab-mcp-server) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is Audio-Launch/audiolab-mcp-server safe to use?
+
Our security agent has analyzed Audio-Launch/audiolab-mcp-server and assigned a Trust Score of 95/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains Audio-Launch/audiolab-mcp-server?
+
Audio-Launch/audiolab-mcp-server is maintained by Audio-Launch. The last recorded GitHub activity is dated 2026-09-20, with 0 open issues.
Are there alternatives to audiolab-mcp-server?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
Deploy audiolab-mcp-server to your cloud
Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.
Maintain this repo? Add a badge to your README
Drop the badge into your GitHub README to show it's tracked on ClaudeWave. Each badge links back to this page and reflects the live Trust Score.
[](https://claudewave.com/repo/audio-launch-audiolab-mcp-server)<a href="https://claudewave.com/repo/audio-launch-audiolab-mcp-server"><img src="https://claudewave.com/api/badge/audio-launch-audiolab-mcp-server" alt="Featured on ClaudeWave: Audio-Launch/audiolab-mcp-server" width="320" height="64" /></a>More MCP Servers
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
An open-source AI agent that brings the power of Gemini directly into your terminal.
Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl! Don't be shy, join here: https://discord.gg/EMgGbDceNQ
The fastest path to AI-powered full stack observability, even for lean teams.