Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Loudness compliance verdicts (EBU R128, ATSC A/85): pass/fail with exact ffmpeg remediation.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"loudcheck": {
"command": "uvx",
"args": [
"loudcheck",
"--mcp"
]
}
}
}在终端中运行这条命令
claude mcp add loudcheck -- uvx loudcheck --mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"loudcheck": {
"command": "uvx",
"args": [
"loudcheck",
"--mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"loudcheck": {
"type": "stdio",
"command": "uvx",
"args": [
"loudcheck",
"--mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 chaoz23 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.3.1)于4个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
同一分类中有:Spec Driven Development, Remote Desktop Commander, 御府和田玉 在售作品查询
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Live in-stock Hetian jade from Yufu, Shanghai: prices, monthly price guide, book a viewing.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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.
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.
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.
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]".
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.
0 = pass, 1 = fail (non-compliant), 2 = error (missing file, no audio stream, no ffmpeg). Gate a delivery on the exit code alone.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.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.loudcheck --schema (the file ships inside the package; a test keeps the two copies in sync).measurement_context.ffmpeg_version. Minimum supported: 5.0. Developed and verified against 8.1.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.
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.
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.
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).
MIT