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MCP server · Communication

io.github.decibri/mcp-listen

Give your AI agents the ability to listen. Microphone capture and speech-to-text.

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About io.github.decibri/mcp-listen

io.github.decibri/mcp-listen is an MCP server in the Communication category: give your AI agents the ability to listen. Microphone capture and speech-to-text. It has been installed 0 times through Conduid.

Install

Claude Code
claude mcp add io-github-decibri-mcp-listen -- npx -y mcp-listen
npx
npx -y mcp-listen

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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README

mcp-listen

Give your AI agents the ability to listen

Microphone capture and speech-to-text tools for MCP-compatible agents.

Tools

Tool Description
list_audio_devices List available microphone input devices
capture_audio Record audio from the microphone and save as WAV
voice_query Capture, transcribe (whisper.cpp), and query a local LLM (Ollama)

Quick Start

Claude Code

claude mcp add mcp-listen npx mcp-listen

Claude Desktop / ChatGPT Desktop / Cursor / Windsurf / VS Code

Add to your MCP configuration:

{
  "mcpServers": {
    "mcp-listen": {
      "command": "npx",
      "args": ["-y", "mcp-listen"]
    }
  }
}

Compatible with Claude Desktop, ChatGPT Desktop, Cursor, GitHub Copilot, Windsurf, VS Code, Gemini, Zed, and any MCP-compatible client.

Global Install

npm install -g mcp-listen

Requirements

For list_audio_devices and capture_audio:

  • Node.js 18+
  • A microphone

For voice_query (optional):

Tool Reference

list_audio_devices

Returns a JSON array of available audio input devices.

Parameters: None

Example response:

[
  { "index": 3, "name": "Microphone (Creative Live! Cam)", "isDefault": true, "maxInputChannels": 2, "defaultSampleRate": 48000 },
  { "index": 4, "name": "Microphone Array (Intel)", "isDefault": false, "maxInputChannels": 2, "defaultSampleRate": 48000 }
]

capture_audio

Records audio from the microphone and saves as a WAV file.

Parameters:

Parameter Type Default Description
duration_ms number 5000 Recording duration in milliseconds (100-30000)
device number system default Device index from list_audio_devices

Example response:

{
  "path": "/tmp/mcp-listen-1712345678901.wav",
  "duration_ms": 5000,
  "sample_rate": 16000,
  "channels": 1,
  "size_bytes": 160044
}

voice_query

Full voice pipeline: capture audio, transcribe with whisper.cpp, send to Ollama, return the response. Entirely offline.

Parameters:

Parameter Type Default Description
duration_ms number 5000 Recording duration in milliseconds (100-30000)
device number system default Device index from list_audio_devices
whisper_model string ggml-base.en.bin Path or filename of Whisper GGML model
language string en Language code for transcription
model string llama3.2 Ollama model name
prompt string You are a helpful assistant. System prompt for the LLM

Example response:

{
  "transcription": "What is the default port for PostgreSQL?",
  "response": "PostgreSQL runs on port 5432 by default.",
  "model": "llama3.2"
}

How It Works

mcp-listen uses decibri for cross-platform microphone capture. No ffmpeg, no SoX, no system audio tools required. Pre-built native binaries with zero setup.

Audio is captured as 16-bit PCM at 16kHz mono, the standard format for speech-to-text engines.

The voice_query tool replicates the pipeline from voxagent: capture audio, transcribe locally with whisper.cpp, and send to a local Ollama LLM. Fully offline, nothing leaves your machine.

Whisper Model Setup

The voice_query tool requires a Whisper GGML model file. Download one:

Linux / macOS:

mkdir -p ~/.mcp-listen/models
curl -L -o ~/.mcp-listen/models/ggml-base.en.bin https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin

Windows (PowerShell):

mkdir "$env:USERPROFILE\.mcp-listen\models" -Force
Invoke-WebRequest -Uri "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin" -OutFile "$env:USERPROFILE\.mcp-listen\models\ggml-base.en.bin"

The model is ~150MB and downloads once. You can also set the WHISPER_MODEL_PATH environment variable to a custom directory.

Ollama Setup

  1. Install Ollama from https://ollama.com
  2. Pull a model: ollama pull llama3.2
  3. Ensure Ollama is running: ollama serve

Known Limitations

  1. Fixed recording duration. You specify how long to record. There is no "stop when I stop talking" mode yet.
  2. voice_query requires Ollama running. If Ollama isn't running, the tool returns a clear error message.
  3. Whisper model downloads on first use. The first voice_query call requires a pre-downloaded model (~150MB).
  4. No streaming. MCP's request/response pattern means the entire recording is captured, then transcribed, then sent to the LLM. No real-time partial results.
  5. Temp files. capture_audio writes WAV files to the system temp directory. They are not automatically cleaned up. voice_query cleans up after itself.

Troubleshooting

Windows: "Error opening microphone" Windows may block microphone access by default. Go to Settings > Privacy & security > Microphone and ensure microphone access is enabled for desktop apps.

Ollama: "Ollama is not running" Some Ollama installations start as a background service automatically. If you see this error, run ollama serve manually or check that the Ollama service is running.

Whisper: "model not found" The whisper model file must be downloaded before first use. See Whisper Model Setup for instructions.

Powered By

  • decibri: Cross-platform microphone capture for Node.js
  • voxagent: Voice-powered terminal agent (inspiration for the voice_query pipeline)

License

Apache-2.0. See LICENSE for details.

Copyright 2026 Decibri

README mirrored from the source repository 4 months ago. The original is authoritative.

Questions

About io.github.decibri/mcp-listen

How do I install io.github.decibri/mcp-listen?

Run claude mcp add io-github-decibri-mcp-listen -- npx -y mcp-listen, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is io.github.decibri/mcp-listen safe to use with an AI agent?

Its trust score is 37 out of 100 (low). Conduid hasn't run static security checks on this repository yet, so review the source yourself before granting it credentials. It has no ConduID identity yet, so agent calls to it are not receipted.

Is io.github.decibri/mcp-listen still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.