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, true-peak and voice-quality analysis of URLs and local files via the hosted AudioLab API.
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。
AUDIOLAB_API_KEY, AUDIOLAB_API_BASE, AUDIOLAB_TIMEOUT_MS标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
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": {
"audiolab": {
"command": "npx",
"args": [
"-y",
"@audiolabtools/mcp-server"
],
"env": {
"AUDIOLAB_API_KEY": "your-value",
"AUDIOLAB_API_BASE": "your-value",
"AUDIOLAB_TIMEOUT_MS": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add audiolab --env AUDIOLAB_API_KEY=your-value --env AUDIOLAB_API_BASE=your-value --env AUDIOLAB_TIMEOUT_MS=your-value -- npx -y @audiolabtools/mcp-server运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"audiolab": {
"command": "npx",
"args": [
"-y",
"@audiolabtools/mcp-server"
],
"env": {
"AUDIOLAB_API_KEY": "your-value",
"AUDIOLAB_API_BASE": "your-value",
"AUDIOLAB_TIMEOUT_MS": "your-value"
}
}
}
}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": {
"audiolab": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@audiolabtools/mcp-server"
],
"env": {
"AUDIOLAB_API_KEY": "your-value",
"AUDIOLAB_API_BASE": "your-value",
"AUDIOLAB_TIMEOUT_MS": "your-value"
}
}
}
}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。
AUDIOLAB_API_BASE envAUDIOLAB_TIMEOUT_MS env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
注册名称已通过域名 audiolab.tools 验证(注册中心会核实域名所有权)
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。
你需要填写:AUDIOLAB_API_KEY
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(0.5.0)于9天前进入注册中心。
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 Node.js。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Intervals MCP
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.
Remote MCP server for intervals.icu run data and analysis
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。
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 measure a public URL or a local file on your machine, but the measuring always happens on the hosted API: the file is read here and uploaded there (see Privacy).
Point your MCP client at the package via npx (nothing to install globally). No account needed for a first score:
{
"mcpServers": { "audiolab": { "command": "npx", "args": ["-y", "@audiolabtools/mcp-server"] } } }
Without a key you are on the keyless trial: 20 calls a month and 3 a minute per IP address, for single analyses of a URL or a local file up to 4 MB. Batches and larger files need a key. A free key gives 1,000 calls and 2 GB a month, no card: sign in at https://audiolab.tools/account, generate one, and add it:
"env": { "AUDIOLAB_API_KEY": "al_live_yourkey" }
fetch + AbortSignal.timeout.AUDIOLAB_API_KEY (see Install). No ffmpeg, no native dependencies.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 150 MB) upload over a signed URL, are analysed, and are swept from storage within the hour. *(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, pause energy, 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, loop, room (reverberation from a clap, a pop, an impulse response or a sweep; pass roomSource), chain (lost packets) or pitch (where a voice sits and how clean it is), answered with only the lenses it needs. These ten 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_loudness with urlanalyze_profile with profile:"voice"analyze_profile with profile:"provenance"analyze_loudness with pathcheck_target with path + target:"spotify"| Var | Default | Purpose |
|---|---|---|
AUDIOLAB_API_KEY | – (optional) | Your API key. Without it: the keyless trial (20 calls a month per IP, no batches, files up to 4 MB). |
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). |
Analysis happens on the AudioLab API, so the audio does reach `audiolab.tools`: a url is fetched server-side, and a local path is read here and uploaded to the API (small files in the request body; larger files via a private one-shot signed upload into EU storage). The API returns numbers only and does not keep your audio: a direct request is decoded and discarded inside the request, and a signed upload is swept from storage within the hour (see https://audiolab.tools/privacy). This package has no telemetry and writes nothing to disk. There is no offline mode, and the engine is not published as an installable package. If audio must never leave the machine, don't use a hosted analyser; the in-browser labs on the website measure on-device, but they are for people, not for agents.
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.node hosted-server.mjs --selftest # verifies the 10 tools + guards; no network
MIT © Nathan Renting