Agent Cam MCP Tool

Physical camera perception, quantitative measurement, and verification tool for MCP AI agents.

1. Overview

Agent Cam MCP Tool gives AI coding agents eyes on the physical world. It interfaces with USB webcams and RTSP/HTTP network camera streams, exposing them to any Model Context Protocol (MCP) compatible agent (Google Antigravity, Claude Code, OpenAI Codex, Cursor).

The Problem

Agents working on embedded systems, robotics, CNC machinery, or 3D printers frequently declare builds or firmware flashes "successful" while the physical hardware remains unresponsive, stuck in a boot loop, or displaying error codes. Traditional agents rely solely on build return codes and serial logs.

The Solution

Agent Cam MCP Tool allows the agent to inspect the physical hardware at any moment:

• Returns images directly as MCP image content (like browser screenshots in IDE agents). Nothing is written to disk by default.

• Returns quantitative numbers alongside visual frames (mean brightness, contrast, lit pixel percentage, dominant colors, edge density, sharpness, and motion level) so agents make decisions on hard evidence.

• Maintains an in-memory RAM ring buffer so agents can retrieve what happened before an error or correlate visual changes with serial logs.

• Includes a local developer dashboard to monitor camera feeds, define named regions of interest, apply privacy masks, and maintain user access control.

Related MCP server: Vision MCP Server

2. Cross-Domain Use Cases

• Embedded Boards: Verify power LEDs, RGB status indicators, seven-segment displays, and OLED boot screens after firmware flashing.

• 3D Printing: Inspect first-layer adhesion, detect spaghetti failures mid-print, and monitor nozzle travel.

• CNC & Laser Cutting: Inspect workpiece alignment, stock clamp clearance, and job completion.

• Robotics: Verify robot arm home positions, gripper actuation, and physical limit switch contact.

• Lab Benches: Read multimeter digits, oscilloscope waveforms, and power supply displays via OCR.

• Environmental Rigs: Monitor automated plant watering, aquariums, heating cycles, and prototyping rigs.

• Soldering & Inspection: Perform macro inspection of solder joints and component placement.

3. Architecture

Agent Cam MCP Tool utilizes a single long-lived daemon architecture to ensure multiple agent sessions never contend for hardware access:

graph TD
    Agent[AI Agent: Antigravity / Claude / Cursor] -->|MCP over stdio| Shim[agent-cam-mcp Stdio Shim]
    Agent -->|MCP over HTTP/SSE| Daemon[Daemon 127.0.0.1:8765]
    Shim -->|Fast Local REST / IPC| Daemon
    Daemon --> CamMgr[Camera Manager]
    CamMgr -->|DirectShow / V4L2 / AVFoundation| USB[USB Webcams]
    CamMgr -->|RTSP / HTTP Stream| IP[IP Cameras]
    CamMgr --> Buffer[RAM Ring Buffer: JPEG 4 FPS]
    Daemon --> Analysis[Analysis Engine: SSIM, Metrics, Watcher]
    Daemon --> Policy[Policy Engine: Rate Limits, Privacy Masks]
    Daemon --> Adapters[Adapters: Serial, Logfile, MQTT]
    Daemon --> WebUI[Dashboard: REST, WebSocket, MJPEG]

Why a Daemon?

Operating systems restrict camera hardware to a single accessing process. If every agent session spawned an independent camera process, conflicts and device locks would occur. The Agent Cam daemon exclusively owns the hardware, while the lightweight stdio shim transparently connects to the daemon, starting it in the background if it is not already running.

4. Key Features

• Direct MCP Image Content: Images are encoded as base64 JPEG/PNG objects directly within tool responses.

• Sub-5ms Image Delivery: Background grabber threads maintain a latest-frame slot in memory, eliminating camera warm-up delays.

• Quantitative Measurement: The measure tool provides deterministic numerical metrics without transmitting heavy image tokens.

• Watch Loops: The watch tool monitors physical feeds until a condition fires (change, motion_start, motion_stop, brightness_above, brightness_below, color_present) or a timeout occurs.

• Baseline Comparisons: Compute SSIM (Structural Similarity Index) and generate visual difference heatmaps against saved reference images.

• Timeline Correlation: Combine buffered frames with timestamped lines from serial COM ports, logfiles, or MQTT topics.

• Privacy & Safety Controls: Permanent privacy masks redact sensitive areas before frames reach agent results or the dashboard. A prominent toggle switch allows instant pausing of agent camera access.

• Local Offline Dashboard: Developer console built with zero external web requests or CDN dependencies.

5. Quick Start (Under 60 Seconds)

Installation

Install via uv or pip:

# Using uv (recommended)
uv tool install agent-cam-mcp

# Or using pip
pip install agent-cam-mcp

With optional extras:

uv tool install "agent-cam-mcp[all]"
# Extras available: [serial], [mqtt], [ocr], [all]

Run Self-Diagnosis

Verify all system dependencies and cameras:

agent-cam-mcp doctor

Register with Your IDE

agent-cam-mcp install --client antigravity

6. Client Configuration

The installer automatically writes absolute executable paths to client configurations to prevent path resolution failures when IDEs are launched from desktop shortcuts.

Google Antigravity

Config location: ~/.gemini/antigravity-ide/mcp_config.json

{
  "mcpServers": {
    "agent-cam": {
      "command": "D:\\Projects\\mcp-tool-development\\agent-cam-mcp-tool\\.venv\\Scripts\\agent-cam-mcp.exe",
      "args": [],
      "env": {},
      "disabled": false
    }
  }
}

Claude Code

Run the registration command:

claude mcp add agent-cam -- "D:\path\to\agent-cam-mcp.exe"

Cursor

Config location: ~/.cursor/mcp.json

{
  "mcpServers": {
    "agent-cam": {
      "command": "D:\\path\\to\\agent-cam-mcp.exe",
      "args": []
    }
  }
}

OpenAI Codex / TOML Clients

Config location: ~/.codex/config.toml

[mcp_servers.agent-cam]
command = "D:\\path\\to\\agent-cam-mcp.exe"
args = []

7. CLI Reference

8. MCP Tools Reference

All tools return structured JSON text blocks alongside image content when applicable.

9. Recommended Agent Instructions

Add the following verification policy to your project instructions (AGENTS.md, GEMINI.md, or CLAUDE.md):

# Physical Verification Policy
Never report physical task completion or hardware success without verification:
1. Call `get_status` to ensure camera streams are active.
2. Call `capture_image` or `measure` to verify physical state (LEDs, displays, moving components).
3. If checking state changes, use `watch` or `compare_to_baseline`.
4. Quote exact observed values in your response (e.g. "Status LED brightness rose from 12.0 to 184.5").
5. Never assume success based solely on compiler output or flash utility logs.

10. Performance Benchmarks

Measured on a standard workstation (Python 3.11, Windows 11):

11. Security and Privacy

• Local Host Binding: Defaults to 127.0.0.1. Non-localhost binding requires explicit flags and prints warnings.

• Header Validation: Strict verification of Host and Origin headers prevents cross-site scripting and DNS rebinding attacks.

• Access Token: Mutating endpoints require an authentication token generated per-installation and stored with user-only permissions.

• Action Allowlist: The run_action tool only runs commands defined in config.json. Subprocesses execute argv lists directly (shell=False).

• Privacy Redaction: Rectangular privacy masks black out pixels in the camera layer before frames reach agents, buffers, or previews.

• Agent Access Pause: A hardware pause toggle instantly revokes agent image access and returns a structured paused_by_user status.

12. Troubleshooting Guide

13. License

Released under the MIT License. Copyright (c) 2026 SK Raihan.