loudcheck
Loudness compliance verdicts against formal broadcast standards (EBU R128, ATSC A/85). Measures audio/video with ffmpeg and returns a pass/fail verdict with exact deltas and remediation parameters.
README
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:
- 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
measuredwith 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. --jsonis the full contract: overallverdict, per-metricmeasured/target/tolerance/delta/passwith the spec citation attached to every gated metric,failuresin plain English, andremediationwith 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 withloudnormusing the delta provided.- MCP: register
loudcheck-mcp(stdio; orloudcheck --mcp, orpython -m loudcheck.mcp_server). Zero-install:uvx --from 'loudcheck[mcp]' loudcheck --mcp. Tools:check_loudness(path, standard)→ same JSON as the CLI, andlist_standards()→ the catalog with citations. Verified againstmcp==1.28.1. tool.jsonat the repo root describes the surface machine-readably — or fetch it live from any install withloudcheck --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
<!-- mcp-name: io.github.chaoz23/loudcheck -->
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。