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webcam-mcp

by luigimasango-dev
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webcam-mcp

MCP server giving coding agents eyes and ears: local webcam stills, mic transcription and speech replies.

License: MIT CI Python 3.11

list_voices call and result

Quick start

Requires Python 3.11+, uv and ffmpeg on PATH. Tested 2026-09-11 from a fresh clone on Windows 11 (POSIX same, minus py):

git clone https://github.com/luigimasango-dev/webcam-mcp.git
cd webcam-mcp
uv sync
uv run server.py --selftest

--selftest prints the device list without needing an MCP client — the fastest way to confirm the install is sane. Without flags the server sits waiting for MCP stdio input; appearing to do nothing until a client connects is normal. Ctrl+C to exit.

Related MCP server: voice-mcp-server

How it works

A coding agent that can read your screen but cannot see you or hear you is missing a whole side of a conversation. This closes that gap for any MCP client on a machine with a camera and a microphone.

Everything runs locally. ffmpeg captures frames, faster-whisper transcribes on CPU, openWakeWord handles the wake word. No audio, video, transcript, or image is sent anywhere. The only network call the server ever makes is a one-time model download from Hugging Face the first time you transcribe something.

Tools

Tool

Purpose

Key args

list_devices

Enumerate cameras and microphones. Call this first when a capture fails

—

capture_frame

One still JPEG from a camera. Returns a file path

device, warmup_seconds=1.5, output_path

capture_stitched

Two cameras in one image, first on top, second below

top_device, bottom_device, warmup_seconds, width

record_audio

Record a mic to 16kHz mono WAV. No transcription

seconds, device, output_path

transcribe_file

Transcribe any existing audio/video file to text, locally

file_path, model_size="base", language

record_and_transcribe

Record the mic and return what was said. Blocks for the duration

seconds, device, model_size, keep_audio=False

list_voices

Text-to-speech voices installed on the machine

—

speak

Say something out loud, or render it to a WAV

text, voice, rate=0, volume=100, save_to

listen_for_wake_word

Wait for a wake phrase, then record and transcribe what follows

timeout_seconds, record_seconds, threshold, device

Captures default to a temp folder with timestamped names. Pass output_path to send them somewhere durable. record_and_transcribe deletes its WAV afterwards unless you pass keep_audio=True. Nothing here prunes old captures automatically — clear that folder occasionally.

Limitations

  • Warmup time is not padding. Webcams open dark and auto-exposure needs a moment; below about one second the frame comes back black or badly exposed. The default is 1.5 seconds for a reason.

  • A failed capture is almost always a device already in use. Zoom, Teams, or a browser tab holding the camera produces an ffmpeg I/O error. The second most common cause is the OS privacy setting blocking desktop apps from the camera or microphone.

  • Whisper base (the default) is the speed/accuracy sweet spot. small is meaningfully better on strong accents at roughly three times the compute. Models cache under the user profile after the first download, load lazily, and stay cached per size for the life of the server process.

  • The default wake model is openWakeWord's pretrained hey_jarvis. At the default 0.5 threshold it is reliable; false positives are not a practical concern.

  • speak uses the legacy SAPI voices by default. They sound robotic; OS natural voices run locally once installed and are picked up automatically.

  • The honest architectural limit is the four-hop pipeline: speech to text, text to model, model to text, text to speech. Slower than a native audio model, and tone/volume/emotion are discarded before the model sees the words. Within a text MCP harness, four hops is the correct design.

  • capture_stitched only earns its keep with a second camera connected; otherwise the second half is a connection spinner.

Development

uv sync
uv run --with pytest python -m pytest tests/ -v

The suite launches the real server over stdio, calls tools/list, and asserts the nine tool names are present. No camera or mic is touched. Pin mcp>=1.28.1 but stay below 2.0 — mcp 2.x renamed mcp.server.fastmcp and the import breaks at server start.

License

MIT. See LICENSE.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources

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