Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Description from the maker (original English)
Edit, analyze and convert audio: loudness, spec checks, denoise, EQ, cuts, BPM, key. No ffmpeg.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"audio": {
"command": "npx",
"args": [
"-y",
"audio",
"--mcp"
]
}
}
}Run this in your terminal
claude mcp add audio -- npx -y audio --mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"audio": {
"command": "npx",
"args": [
"-y",
"audio",
"--mcp"
]
}
}
}Paste into .vscode/mcp.json in your project
{
"servers": {
"audio": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"audio",
"--mcp"
]
}
}
}These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
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 5, 2026
The registry name was verified with the GitHub account audiojs, which owns the repo
Code last changed yesterday
Uses MIT
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Needs the server’s tool list first; this check starts in the next step
Compared only with servers in this prototype, not the whole registry.
It passed 3 of 3 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.
The registry does not list a required key, though some servers ask you to sign in on first use.
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).
The GitHub repo last changed yesterday, and the latest version (2.10.0) reached the registry 33 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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.
Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.
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.
_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