Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Descripción del creador (original en inglés)
Edit, analyze and convert audio: loudness, spec checks, denoise, EQ, cuts, BPM, key. No ffmpeg.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"audio": {
"command": "npx",
"args": [
"-y",
"audio",
"--mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add audio -- npx -y audio --mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"audio": {
"command": "npx",
"args": [
"-y",
"audio",
"--mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"audio": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"audio",
"--mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub audiojs, dueña del repo
Último cambio de código: anteayer
Usa MIT
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 3 de 3 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez anteayer, y la última versión (2.10.0) llegó al registro hace 34 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Figma Context MCP, Claude Real Video, Figma MCP Server
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Let any LLM watch a video locally — and search everything it has ever watched.
The Figma MCP server brings Figma design context directly into your AI workflow.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
_High-level audio manipulations: loading, playback, analysis and editing. 140+ plugins: denoise, dynamics, EQ, filters, effects, reverb, time and pitch, spatial, synthesis, music analysis._
npm i audio
import audio from 'audio'
let song = audio('song.mp3') song.play() // the speakers, as a page plays it: no ffmpeg, no player to spawn song.pause(); song.seek(60); song.resume()
let voice = audio('voice.wav').trim().normalize('podcast') await voice.save('clean.mp3') let { pass, rules } = await voice.check('podcast') // each rule of Apple's spec, measured
<script type="module">
import audio from 'https://esm.sh/audio' audio('./song.mp3').trim().normalize().fade(0.5, 2).clip({ at: 60, duration: 30 }).play()
Audio anywhereimport 'audio/polyfill' // the page's HTMLAudioElement, in Node, Bun and Deno
let song = new Audio('song.mp3') song.onended = () => console.log('done') await song.play() song.currentTime = 30; song.volume = 0.5; song.loop = true
Events, their order, promises and errors as Chromium's own element has them, compared observation by observation (test/polyfill.js). import { Audio } from 'audio/polyfill' leaves the global alone.
npm i -g audio # or: npx audio …
audio voice.wav trim normalize podcast save clean.mp3 audio clean.mp3 check podcast
A fail exits 1, so the same line guards a build: Check in CI.
npx skills add audiojs/audio
npx add-mcp "npx -y audio --mcp" # asks which of your agents: Claude Code, Codex, Cursor, Gemini CLI, Kimi Code, Pi, OpenCode, Zed…
Prompt: make ~/Desktop/interview.m4a podcast-ready and tell me the loudness before and after
One agent at a time: claude mcp add audio -- npx -y audio --mcp, qwen mcp add audio npx -y audio --mcp, droid mcp add audio "npx -y audio --mcp".
npx audio --bridge
#
#
Open the playground, every edit of it a line of audio code, and connect it to the bridge with the key (once: the bridge keeps it), and its chat runs an agent of yours, which measures, looks at, edits and plays the sound open there, and finds which edit did what: it measures the sound before and after each, and what each took out; each tab keeps its conversations, each with the agent, and its model, picked under the message. The bridge finds Claude Code, Codex, Pi, Gemini CLI, Qwen Code, Kimi Code, OpenCode, Kilo Code, Cline, Goose, Factory Droid, Cursor, Augment, Kiro and Mistral Vibe on PATH; any other that speaks ACP runs by its command line, --agent "my-agent --acp".
Any MCP agent gets the playground's tools (state, measure, look, edit, select, play, check, …), the running bridge found by itself: npx add-mcp "npx -y audio --mcp --playground".
An agent thinks with the model its own settings name: Pi takes local ones for good from ollama launch pi --config; Claude Code takes any Anthropic-compatible endpoint from the bridge's environment:
ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_API_KEY= ANTHROPIC_MODEL=qwen3-coder npx audio --bridge
| Models | ANTHROPIC_BASE_URL, with the provider's key as ANTHROPIC_AUTH_TOKEN |
|---|---|
| Ollama | http://localhost:11434, token ollama (docs) |
| Z.ai GLM | https://api.z.ai/api/anthropic (docs) |
| Kimi | https://api.moonshot.ai/anthropic (docs) |
| Qwen | https://coding-intl.dashscope.aliyuncs.com/apps/anthropic, Coding Plan (docs) |
| DeepSeek | https://api.deepseek.com/anthropic (docs) |
The playground in any page, its script the element's own text: it runs where the reader scrolls to it, plays, takes edits, and opens whole in the playground. Nothing of the reader's is kept.
<script type="module" src="https://audio.js.org/embed.js"></script>
audio('chime.wav') .trim() .normalize(-1)
What it shows, the frame takes as one JSON: the element's ui attribute, with these attributes over it, each a key of it. panel: code (default), edits (its steps, each with its settings) or none; display: wave or spec; controls: the parts shown, each in its own place: over the picture sound (its name, a menu of its ways in: a file, a take, a sample), link and name (the way to the playground), ruler (the time row; left out, none: the big time says it), look (a band's dropdown of how it draws); in the bar play record loop clock history message heard, a level (level, or what it reads: peak rms loudness), rate channels depth before, view (a switch: the waveform, the spectrogram), open and save (a file of the reader's in; the result out, in the format picked from its menu) — without it, all but sound, view, open and save; ops: edits a press away, one button after the history, a menu of them with their values, as trim normalize(-1) stretch(1.25) reverse (a call's own arguments, or the defaults the menus write); background: the ground it sits on and fades into, a CSS colour, or page, the page's own under the element (the element reads it; a frame can't); height in px. A sound named by its path from the site's root (audio('/sounds/take.mp3')) is that site's file. The shows until the frame comes, and where scripts don't run. Every key, zoom and overshoot too: playground/README.md.
// master a raw take
let a = audio('raw-take.wav') a.trim(-30).normalize('podcast').fade(0.3, 0.5) await a.save('clean.wav')
// full restoration chain via ecosystem plugins (see API › Plugins) a.gate(-45).dehum().deesser().compressor({ threshold: -18 }).limiter({ ceiling: -1 })
// cut 2:00–2:15, smooth the splice a.remove({ at: 120, duration: 15 }).fade(0.1, { at: 120 })
// find clipped blocks let clips = await a.stat('clipping')
// does it pass? each rule of the spec, measured let { pass, rules } = await a.check('podcast') // acx, podcast, spotify, broadcast, netflix
// master a song to a reference track: its tone (mid and side), width and loudness, under -1 dBTP
audio('mix.wav').master(await audio('reference.wav')).save('master.wav')
// audiobook chapter for ACX: RMS -23..-18 dB, peaks under -3 dB, floor under -60 dB, room tone at each end, 44.1 kHz let ch = audio('chapter-01.wav') .highpass(80).omlsa({ gMin: -12 }).compressor({ threshold: -24, ratio: 2.5 }) .normalize(-20, 'rms', { ceiling: -3.5 }) .trim().pad(1.5, 2).roomtone() // room tone, not digital silence .resample(44100) console.log(await ch.check('acx')) // passed 10 of 10 real narrations (.work/pro.md) await ch.save('chapter-01.mp3', { bitrate: 192 })
// tighten pauses in a talking-head video, then hand the cuts to the video editor let talk = audio('talk.mp4').shrink(0.3) await talk.save('talk.m4a') // the sound, cut await talk.save('talk.edl') // the same cuts for the picture (Premiere, Resolve)
// podcast montage
let ep = audio([intro, interview.trim().normalize('podcast'), outro], { crossfade: 0.5 }) await ep.save('episode.mp3')
// voiceover over music music.gain(-12).mix(voice, { at: 2 })
// ringtone: the chorus + fades audio('song.mp3').crop({ at: 45, duration: 30 }).fade(0.5, 2).normalize().save('ringtone.mp3')
// split an audiobook into chapters let [ch1, ch2, ch3] = audio('audiobook.mp3').split(1800, 3600)
// glitch: stutter + reverse let v = a.clip({ at: 1, duration: 0.25 }) audio([v, v, v, v]).reverse({ at: 0.25, duration: 0.25 })
// waveform bars — and progressively, as it decodes
let [mins, peaks] = await a.stat(['min', 'max'], { bins: canvas.width }) a.on('data', ({ delta }) => appendBars(delta.max[0], delta.min[0]))
// features for ML let mfcc = await a.stat('cepstrum', { bins: 13 }) let [loud, rms] = await a.stat(['loudness', 'rms'])
// notes, chords, key let notes = await a.stat('notes') // [{time, duration, freq, midi, note, clarity}] let chords = await a.stat('chords') // [{time, duration, label, root, quality, confidence}] let key = await a.stat('key') // {tonic, mode, label, confidence}
// mic take
let a = audio() a.record() // …later a.stop() a.trim().normalize()
// tone — any t => sample function let tone = audio.from(t => Math.sin(440 * Math.PI * 2 * t), { duration: 2 })
// sonify data let s = audio.from(t => Math.sin((200 + data[t / 0.2 | 0]) * Math.PI * 2 * t) * 0.5, { duration: data.length * 0.2 })
a.gain(t => -12 * (0.5 + 0.5 * Math.cos(t * Math.PI * 4))) // 2Hz tremolo in dB
a.lowpass(t => 400 + 4000 * t) // filter sweep a.pan({ t: [0, 2, 4], v: [-1, 1, -1] }) // curve L→R→L over 4s, serializable music.ducker({ key: voice }) // sidechain (plugin)
// stream to network — encode/playback during decode
for await (let chunk of audio('2hour-mix.flac').highpass(40)) socket.send(chunk[0].buffer)
// serialize edits, restore later let json = JSON.stringify(a) // { source, edits, ... } let b = audio(JSON.parse(json)) // re-decode + replay edits
| Method | Description