Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"webear": {
"env": {
"WEBEAR_BASE_URL": "http://localhost:5000",
"CODEDSWITCH_API_KEY": "your-key-here"
},
"args": [
"webear"
],
"command": "npx"
}
}
}Are you the author?
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An MCP server that lets AI coding assistants capture, analyze, and describe live audio from a running web application — not a file on disk, not the physical microphone. The actual AudioContext output your app is rendering right now.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'webear' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked webear against OSV.dev.
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Give your AI coding assistant ears.
An MCP server that lets AI coding assistants capture, analyze, and describe live audio from a running web application — not a file on disk, not the physical microphone. The actual AudioContext output your app is rendering right now.
"The beat sounds muddy" → your AI captures 3 seconds, measures the spectral centroid at 580 Hz with 45% energy below 250 Hz, and tells you exactly why.
| Tool | Description |
|---|---|
capture_audio | Record a short clip (500ms–30s) of what your web app is outputting right now |
analyze_audio | Signal analysis: RMS, peak dB, clipping, spectral centroid, frequency bands, BPM, timing jitter |
describe_audio | Plain-English AI description — "the kick is boomy with heavy sub buildup around 80 Hz" |
diff_audio | Compare two captures and flag what changed — loudness, tone, timing, clipping |
Browser (Web Audio API)
↓ MediaRecorder taps the AudioContext output node
↓ Uploads WebM blob via HTTP POST
Express Middleware (your dev server)
↓ Stores captures in memory, dispatches commands via SSE
MCP Server (stdio — runs inside your IDE)
↓ Retrieves captures, sends to CodedSwitch analysis API
AI Coding Assistant
→ "Your bass band is 42% of the mix (high), spectral centroid
is 580 Hz (muddy), and timing jitter is 23ms — the scheduler
is drifting under load."
The key difference from every other audio MCP: this taps the Web Audio graph directly, bypassing room acoustics, microphone hardware, and the need to export files.
npm install webear
import express from 'express'
import { webearMiddleware } from 'webear/middleware'
const app = express()
app.use(express.json())
// Mount the audio debug bridge (automatically disabled in production)
app.use('/api/webear', webearMiddleware())
app.listen(5000)
Option A — auto-detect everything (Tone.js or raw Web Audio)
import WebEar from 'webear/client'
WebEar.init()
Option B — explicit AudioContext
const ctx = new AudioContext()
const masterGain = ctx.createGain()
masterGain.connect(ctx.destination)
WebEar.init({ audioContext: ctx, outputNode: masterGain })
Option C — Tone.js project
import * as Tone from 'tone'
WebEar.init({ toneJs: true })
Option D — Three.js WebGL Game
import * as THREE from 'three'
const listener = new THREE.AudioListener()
camera.add(listener)
WebEar.init({ tapNode: listener.getInput() })
Option E — plain script tag
<script src="node_modules/webear/client-snippet.js"></script>
<script>WebEar.init()</script>
Claude Code (.mcp.json in project root):
{
"mcpServers": {
"webear": {
"command": "npx",
"args": ["webear"],
"env": {
"WEBEAR_BASE_URL": "http://localhost:5000",
"CODEDSWITCH_API_KEY": "your-key-here"
}
}
}
}
Cursor (.cursor/mcp.json):
{
"mcpServers": {
... [View full README on GitHub](https://github.com/asume21/webear#readme)