loudcheck

Name: loudcheck
Author: chaoz23

by chaoz23version 0.3.1i★ –iRuns on your computer

Registry nameio.github.chaoz23/loudcheck

Install

Overview

Description from the maker (original English)

Loudness compliance verdicts (EBU R128, ATSC A/85): pass/fail with exact ffmpeg remediation.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "loudcheck": {
      "command": "uvx",
      "args": [
        "loudcheck",
        "--mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add loudcheck -- uvx loudcheck --mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "loudcheck": {
      "command": "uvx",
      "args": [
        "loudcheck",
        "--mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "loudcheck": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "loudcheck",
        "--mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account chaoz23, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32684 servers in this category
Published to the registry more recently than 15% of the other 32684 servers in this category
Basic checks equal to or better than 100% of the other 32684 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is loudcheck safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.3.1) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Master CSS

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

loudcheck

A loudness compliance verdict, not raw meter output. loudcheck measures a media file with ffmpeg and answers the question that actually matters — *does this file pass the spec?* — against formal published standards:

Agents start here → [SKILL.md](SKILL.md) — when to call this, worked examples, MUST/MUST NOTs. Family contract: FAMILY.md.

EBU R 128 (European broadcast: −23.0 LUFS ±0.5 LU, max −1 dBTP)
ATSC A/85 (US television: −24 LKFS ±2 dB, true peak below −2 dBTP)
BS.1770 (measure-only: BS.1770 defines no compliance target, so this mode returns verdict measured with the numbers and no judgment)

$ loudcheck master.wav --standard EBU_R128

FAIL — EBU R 128 ✗ integrated -19.4 LUFS (target -23.0 ±0.5, delta +3.6) ✓ true peak -16.3 dBTP (max -1.0) · LRA 0.0 LU (informational) → apply -3.6 LU gain to reach -23.0 LUFS (e.g. ffmpeg -af volume=-3.6dB, or loudnorm I=-23)

Ships as a CLI and an MCP tool over one engine, so agents and humans get the identical verdict.

Why this exists

An agent (or an engineer) can run ffmpeg's ebur128 filter and get numbers. What it can't get from a shell is the *verdict* — that requires knowing the standard's target, tolerance, and gating, and interpreting integrated loudness vs. LRA vs. true peak against them. Loudness is one of the most common causes of delivery rejection, and the gap is not measurement — it's the standards-aware answer. That's the whole tool.

Install

Requires Python 3.10+ and ffmpeg 5.0+ on PATH.

uvx loudcheck file.wav          # zero-install run (requires ffmpeg >= 5.0 on PATH)

pip install loudcheck # CLI pip install "loudcheck[mcp]" # + MCP server

From a clone: pip install -e ".[mcp,dev]".

CLI

loudcheck file.wav                          # EBU R128 by default

loudcheck file.mp4 --standard ATSC_A85 # first audio stream of a video loudcheck file.wav --json # full structured verdict loudcheck file.wav --standard BS_1770 # measurement only, no gates loudcheck file.mov --all-streams # verdict every audio track loudcheck file.mov --stream 1 # a specific audio track loudcheck file.wav --detailed # + max momentary / short-term loudcheck masters/ --standard EBU_R128 # batch a directory -> table loudcheck a.wav b.wav c.wav # batch multiple files loudcheck --schema # print the tool definition

Batch mode prints one line per file (plus remediation for fails) and a summary; --json in batch emits an array. Exit code is 1 if *any* file fails.

For agents

Exit codes carry the verdict: 0 = pass, 1 = fail (non-compliant), 2 = error (missing file, no audio stream, no ffmpeg). Gate a delivery on the exit code alone.
`--json` is the full contract: overall verdict, per-metric measured/target/tolerance/delta/pass with the spec citation attached to every gated metric, failures in plain English, and remediation with the exact correction — a fail 2.3 LU over target tells you to apply −2.3 LU gain and hands you the ffmpeg incantation. This tool never applies the fix (measurement and verdict only); the agent one-shots it with loudnorm using the delta provided.
MCP: register loudcheck-mcp (stdio; or loudcheck --mcp, or python -m loudcheck.mcp_server). Zero-install: uvx --from 'loudcheck[mcp]' loudcheck --mcp. Tools: check_loudness(path, standard) → same JSON as the CLI, and list_standards() → the catalog with citations. Verified against mcp==1.28.1.
`tool.json` at the repo root describes the surface machine-readably — or fetch it live from any install with loudcheck --schema (the file ships inside the package; a test keeps the two copies in sync).
ffmpeg version is part of the contract: every verdict includes measurement_context.ffmpeg_version. Minimum supported: 5.0. Developed and verified against 8.1.

The scope guardrail (read before contributing)

Only formal, stable standards live in this repo; per-platform delivery templates (Netflix, DPP, Apple TV+, Amazon, broadcaster specs) never do. Platform specs change unilaterally and cover far more than loudness — the moment they enter, this stops being a near-zero-maintenance community tool and becomes a yearly-maintenance product. If a PR adds a target that a platform can change on its own, it belongs in a separate template layer built *on top of* this primitive, not here.

Contributions of additional *formal* standards (e.g. a plain ITU-R BS.1770 mode) are welcome: a standard is pure data in loudcheck/standards.py — targets, tolerances, and citations. No code changes required.

How it measures

One ffmpeg pass with loudnorm=print_format=json (analysis mode) yields integrated loudness, loudness range, true peak (oversampled dBTP per BS.1770), and the gating threshold. The test suite cross-checks loudnorm's reading against ffmpeg's independent ebur128 implementation — the two must agree within 1 LU for CI to pass, so an ffmpeg release that changes filter behavior is caught by the suite, not by users.

Verification corpus

pytest generates calibrated test tones on the fly (no binaries in the repo): per BS.1770's calibration statement, a mono 997 Hz sine at 0 dBFS reads −3.01 LKFS, so tones are generated at exact known loudness — compliant, too loud, too quiet, and true-peak-hot — and every verdict must match its known expectation.

Out of scope, permanently

Loudness *correction* (use ffmpeg loudnorm with the delta this tool gives you) · full-file QC (codec/colour/cadence) · real-time monitoring · GUIs · platform delivery templates (see guardrail).

License

MIT

loudcheck

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Master CSS

README from the maker

loudcheck

Why this exists

Install

CLI

For agents

The scope guardrail (read before contributing)

How it measures

Verification corpus

Out of scope, permanently

License