UVC PTZ Camera

uvc-ptz-camera-mcp

An MCP server for USB (UVC) pan/tilt/zoom cameras: aim them, nudge them, sweep them smoothly, zoom, and look through them — with every move confirmed by comparing the picture before and after.

No account, no cloud, no vendor SDK. One device, one USB cable.

camera_status       the camera, its axes and ranges, and whether it is real
aim                 point an axis at an absolute value, confirmed from the picture
nudge               move relative to where the device says it is
sweep               a smooth timed move, streamed at 15 Hz
zoom                set zoom as a multiplier (1x .. the camera's maximum)
recentre            every axis back to its default
look                one frame, returned as an image
aim_learn           remember "this direction is the desk"
aim_list            what has been recorded for this camera
go_to               point at a recorded direction
plan_shot           compile a multi-step shot and report what it would do, moving nothing
run_shot            execute a multi-step move, verified after every waypoint
mark_view           store the current picture under a label
check_view          has the picture changed since that label?
track_start         follow a subject: detect, measure, take a step, look again
track_status        what the follow loop is doing, iteration by iteration
track_stop          stop following and report what it saw and what it moved

Composing a shot is plan_shot then run_shot. Planning compiles exactly the steps the executor will compile, reports the per-step schedule, how far each axis travels and how long it takes, and touches nothing — so an agent can iterate on "a panorama, then zoom in on me" against the camera's real ranges before anything moves, instead of finding out afterwards.

Following

track_start runs a loop that keeps the camera pointed at a subject: detect it, measure how far it is from the centre of the frame, take a bounded step, look again. It decides only from the picture, for the same reason every other move here does — this camera's own report of where it is pointing cannot be trusted. It stops on its own when the subject is lost, when it settles inside the dead zone, when the camera stops answering, or after max_seconds, and track_status lists every iteration with the offset it measured and the step it applied.

Two quantities are mount-dependent rather than device-dependent, so they are explicit parameters instead of clever guesses: gain, degrees of movement per pixel of offset, and invert, which flips the direction. A wrong choice shows up honestly as a residual error that grows, and a single iteration is capped at 25 degrees, so a wrong gain converges slowly rather than throwing the camera across its whole range in one command.

Detection uses OpenCV's bundled face cascade (pip install 'uvc-ptz-camera-mcp[tracking]'), which needs no model download, no account and no network at first use. It finds faces: this follows a person, not a car or a dog. The loop and its arithmetic are tested against the simulator, but no part of follow mode has ever run against real hardware — treat it as the least proven thing here.

Why this exists

This class of camera reports positions it never moved to. Measured on the reference device (a DJI Osmo Pocket 4P in webcam mode, over its standard UVC controls):

• a command to pan to 180 read back 180 while the video proved the camera had not moved at all;
• a whole 32-second run read back 0 on every sample while the frame demonstrably changed;
• pan 120 read back 119, pan 200 read back 199.

An agent acting on those values reports moves that never happened. So this server treats every device-reported value as a hint — returned, labelled, never trusted — and decides moved by comparing frames.

The threshold is not a guess either. It was calibrated on labelled hardware frames: every "same view" pair scored ≤ 0.104 (a 96-second quiet baseline, and two commands the hardware ignored), every "view changed" pair scored ≥ 0.624 (a pan, a tilt, a 12× zoom, two aim steps). The shipped threshold is the midpoint, 0.364 — a 6× separation — and the test suite asserts it still separates them. The labelled pairs and the values the metric produced are kept in the repository (tests/fixtures/) so the number can be re-checked rather than believed — as measurements, not pictures: no frame or video taken by a camera is committed here, and a test fails if one appears. The measurements behind the calibration are in docs/measurements.md.

What keeps the agent honest

• moved comes from the picture. device_reported is returned beside it, labelled a hint.
• A move that produced no change is an error, not a quiet success — after a bounded retry, the tool fails naming what was requested and what was observed.
• Already being at the target is success without movement: moved: false, no error.
• Blocking work never runs on the event loop (COM calls, ffmpeg captures), pinned by a test that records which thread the camera was touched on.
• Observation is explicit: nothing takes a picture except a tool you called.
• A refused write aborts a shot rather than continuing to drive a camera that stopped listening.

Install

uvx uvc-ptz-camera-mcp            # run without installing
pip install uvc-ptz-camera-mcp    # or install it

For a real camera on Windows you also need the DirectShow extras:

pip install "uvc-ptz-camera-mcp[dshow]"

Then point your host at it — --print-config emits the snippet with the right interpreter:

uvc-ptz-mcp --print-config
uvc-ptz-mcp --list-devices        # what to pass to --device
uvc-ptz-mcp --backend simulator   # try it with no camera attached

Where to put it

Claude Desktop, Cursor, VS Code and friends all take the same shape — this is the whole configuration, because there is nothing to authenticate:

{
  "mcpServers": {
    "ptz-camera": {
      "command": "uvx",
      "args": ["uvc-ptz-camera-mcp"]
    }
  }
}

Add "--device", "Osmo" (any substring of the name --list-devices prints) when the machine has more than one camera, and "--backend", "simulator" to work with none. Both can also be set in the environment instead of the args — UVC_PTZ_DEVICE, UVC_PTZ_BACKEND and UVC_PTZ_STATE_DIR — which is what the Claude Desktop bundle uses, and which survives a host restart without editing its config again. A flag wins over the environment, and an empty value means "not set" (a host that leaves an option blank writes "", not nothing). Claude Desktop also accepts the .mcpb bundle attached to each release as a one-click install.

Modes, and how you can tell which one you are in

Every result carries "simulated": true or false, and camera_status reports the reason when the simulator is standing in. Nothing quietly pretends to be hardware: a caller must never believe it is driving a camera when it is driving a model.

The simulator is calibrated from the reference device, not invented: ~0.4 s from write to first motion, a pan completing in about a second, a 4× zoom in about 2.5 s, silently dropped writes, and a read-back that echoes a request the hardware never applied. It renders frames by cropping a wide panorama, so a simulated pan genuinely changes the pixels — a simulator with a static picture would let verification pass vacuously.

Honest limitations

• The real-device path is Windows-only (DirectShow). A Linux backend would use v4l2; the interface is in place, the implementation is not written.
• No exposure, focus or white balance. The reference camera exposes no Processing Unit at all — only pan/tilt/roll/zoom.
• The vendor Extension Unit is unreachable on Windows, so features that live there (on the reference camera, its built-in subject tracking) cannot be driven from software. Measured: IKsControl is refused on the device filter, IKsTopologyInfo lists three nodes and no vendor node, and CreateNodeInstance fails on all of them. Linux can reach it through uvcvideo's UVCIOC_CTRL_QUERY; that is a separate backend.
• A moving subject looks like a moving camera. Frame comparison cannot tell the two apart; the metric is calibrated so that a static scene behaves, and check_view is there for judging a view rather than a move.
• One unusable device must not hide the others. A registered-but-unavailable virtual camera raised when its DirectShow moniker was bound, which aborted a listing that should have returned two working cameras. Enumeration now skips what it cannot load and says so, and the same rule applies inside the backend when it looks for the camera you named.
• Measured latency sets the ceiling: ~0.4 s from command to motion, so control loops run at a couple of hertz, not tens. That is the hardware's floor, not the software's.
• The DirectShow path is tested but dormant. The reference device was returned partway through development, so the hardware path is exercised up to its interface and typed correctly against it, while the simulator carries the test load. Treat the first run against a real camera as the true acceptance test.

Development

pip install -e ".[dshow]" numpy pytest pytest-asyncio ruff
python -m pytest -q                     # 39 tests, three layers
python -m ruff check . && python -m ruff format --check .

The suite is layered deliberately: the tool surface in-process against the simulator (mapping, validation, honest failure), pure unit tests for the compiler and the metric, and a real stdio handshake as a subprocess — including one run with a device name that cannot exist, because a server that dies at startup is invisible to every host and directory that lists it.

Reference device

Built against a DJI Osmo Pocket 4P in webcam mode: VID_2CA3 / PID_0023, exposing pan −38…215°, tilt −33…105°, roll ±35°, zoom 100…1200 (1×–12×) as standard UVC camera controls. Any UVC PTZ camera exposes the same surface; the ranges are read from the device at startup rather than assumed, and nothing vendor-specific is hard-coded.

Not affiliated with, endorsed by, or supported by DJI.