OpticMCP

A Model Context Protocol (MCP) server that provides camera/vision tools for AI assistants. Connect to cameras and capture images for use with LLMs.

Vision

OpticMCP aims to be a universal camera interface for AI assistants, supporting any camera type:

• USB Cameras ✅
• IP/Network Cameras ✅ - RTSP, HLS, MJPEG streams
• Screen Capture ✅ - Desktop/monitor capture
• HTTP Images ✅ - Download images from URLs
• QR/Barcode Decoding ✅ - Decode QR codes and barcodes
• Image Analysis ✅ - Metadata, stats, histograms, dominant colors
• Image Comparison ✅ - SSIM, MSE, perceptual hashing, visual diff
• Detection ✅ - Face detection, motion detection, edge detection
• Raspberry Pi Cameras (Planned) - CSI camera modules
• Mobile Cameras (Planned) - Phone camera integration

Current Features

USB Cameras

• list_cameras - Scan and list all available USB cameras
• save_image - Capture a frame and save directly to a file

Camera Streaming

• start_stream - Start streaming a camera to a localhost HTTP server (MJPEG)
• stop_stream - Stop streaming a camera
• list_streams - List all active camera streams

Multi-Camera Dashboard

• start_dashboard - Start a dynamic dashboard that displays all active camera streams in a responsive grid
• stop_dashboard - Stop the dashboard server

RTSP Streams

• rtsp_save_image - Capture and save a frame from an RTSP stream
• rtsp_check_stream - Validate RTSP stream and get properties

HLS Streams (HTTP Live Streaming)

• hls_save_image - Capture and save a frame from an HLS stream
• hls_check_stream - Validate HLS stream and get properties

MJPEG Streams

• mjpeg_save_image - Capture a frame from an MJPEG stream (common in IP cameras, ESP32-CAM)
• mjpeg_check_stream - Validate MJPEG stream availability

Screen Capture

• screen_list_monitors - List all available monitors/displays
• screen_save_image - Capture full screenshot of a monitor
• screen_save_region - Capture a specific region of the screen

HTTP Images

• http_save_image - Download and save an image from any URL
• http_check_image - Check if a URL points to a valid image

QR/Barcode Decoding (requires libzbar)

• decode_qr - Decode QR codes from an image
• decode_barcode - Decode barcodes (EAN, UPC, Code128, etc.)
• decode_all - Decode all QR codes and barcodes from an image
• decode_and_annotate - Decode and save annotated image with bounding boxes

Image Analysis

• image_get_metadata - Extract image metadata including EXIF data
• image_get_stats - Calculate brightness, contrast, sharpness
• image_get_histogram - Generate color histogram with optional visualization
• image_get_dominant_colors - Extract dominant colors using K-means clustering

Image Comparison

• image_compare_ssim - Compare images using Structural Similarity Index
• image_compare_mse - Compare images using Mean Squared Error
• image_compare_hash - Compare images using perceptual hashing (phash, dhash, ahash)
• image_get_hash - Generate perceptual hash for an image
• image_diff - Create visual diff highlighting differences
• image_compare_histograms - Compare images by color histograms

Detection

• detect_faces - Detect faces using Haar cascades or DNN
• detect_faces_save - Detect faces and save annotated image
• detect_motion - Detect motion between two frames
• detect_edges - Detect edges using Canny, Sobel, or Laplacian
• detect_objects - Detect common objects using MobileNet SSD

Requirements

• Python 3.10+
• USB camera connected to your system

Installation

From PyPI (Recommended)

pip install optic-mcp

Or with uv:

uv pip install optic-mcp

From Source

# Clone the repository
git clone https://github.com/Timorleiderman/OpticMCP.git
cd OpticMCP

# Install dependencies with uv
uv sync

Usage

Running the MCP Server

If installed from PyPI:

optic-mcp

Or with uvx (no installation required):

uvx optic-mcp

Running from Source

uv run optic-mcp

MCP Configuration

Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "optic-mcp": {
      "command": "uvx",
      "args": ["optic-mcp"]
    }
  }
}

OpenCode

Add to your opencode.json (in .opencode/ in your project directory or ~/.opencode/ globally):

{
  "mcp": {
    "optic-mcp": {
      "type": "local",
      "command": ["uvx", "optic-mcp"]
    }
  }
}

Other MCP Clients

Using uvx (recommended - no installation required):

{
  "mcpServers": {
    "optic-mcp": {
      "command": "uvx",
      "args": ["optic-mcp"]
    }
  }
}

Using pip installation:

{
  "mcpServers": {
    "optic-mcp": {
      "command": "optic-mcp"
    }
  }
}

From source:

{
  "mcpServers": {
    "optic-mcp": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/OpticMCP", "optic-mcp"]
    }
  }
}

Tools

list_cameras

Scans for available USB cameras (indices 0-9) and returns their status.

[
  {
    "index": 0,
    "status": "available",
    "backend": "AVFOUNDATION",
    "description": "Camera 0 (AVFOUNDATION)"
  }
]

save_image

Captures a frame and saves it to disk.

Parameters:

• file_path (str) - Path where the image will be saved
• camera_index (int, default: 0) - Camera index to capture from

Returns: Success message with file path

Streaming Tools

Stream cameras to a local HTTP server for real-time viewing in any browser.

start_stream

Start streaming a camera to a localhost HTTP server. The stream uses MJPEG format which is widely supported.

Parameters:

• camera_index (int, default: 0) - Camera index to stream
• port (int, default: 8080) - Port to serve the stream on

Returns: Dictionary with stream URLs and status

{
  "status": "started",
  "camera_index": 0,
  "port": 8080,
  "url": "http://localhost:8080",
  "stream_url": "http://localhost:8080/stream"
}

Usage:

• Open http://localhost:8080 in a browser to view the stream with a simple UI
• Use http://localhost:8080/stream for the raw MJPEG stream (can be embedded in other applications)

stop_stream

Stop streaming a camera.

Parameters:

• camera_index (int, default: 0) - Camera index to stop streaming

Returns: Dictionary with status

list_streams

List all active camera streams.

Returns: List of active stream information including URLs and ports

Dashboard Tools

start_dashboard

Start a dynamic multi-camera dashboard server. The dashboard automatically detects all active camera streams and displays them in a responsive grid layout.

Parameters:

• port (int, default: 9000) - Port to serve the dashboard on

Returns: Dictionary with dashboard URL and status

{
  "status": "started",
  "port": 9000,
  "url": "http://localhost:9000"
}

Usage:

1. Start one or more camera streams with start_stream
2. Start the dashboard with start_dashboard
3. Open http://localhost:9000 in a browser
4. The dashboard auto-updates every 3 seconds to detect new/removed streams

stop_dashboard

Stop the dashboard server.

Returns: Dictionary with status

RTSP Tools

Note: RTSP functionality has not been tested with real RTSP hardware/streams. It is implemented but may require adjustments for specific camera vendors.

rtsp_save_image

Captures a frame from an RTSP stream and saves it to disk.

Parameters:

• rtsp_url (str) - RTSP stream URL (e.g., rtsp://ip:554/stream)
• file_path (str) - Path where the image will be saved
• timeout_seconds (int, default: 10) - Connection timeout

Returns: Success message with file path

rtsp_check_stream

Validates an RTSP stream and returns stream information.

Parameters:

• rtsp_url (str) - RTSP stream URL to validate
• timeout_seconds (int, default: 10) - Connection timeout

Returns: Dictionary with stream status and properties (width, height, fps, codec)

HLS Tools

hls_save_image

Captures a frame from an HLS stream and saves it to disk.

Parameters:

• hls_url (str) - HLS stream URL (typically ending in .m3u8)
• file_path (str) - Path where the image will be saved
• timeout_seconds (int, default: 30) - Connection timeout

Returns: Success message with file path

hls_check_stream

Validates an HLS stream and returns stream information.

Parameters:

• hls_url (str) - HLS stream URL to validate
• timeout_seconds (int, default: 30) - Connection timeout

Returns: Dictionary with stream status and properties (width, height, fps, codec)

MJPEG Tools

mjpeg_save_image

Captures a frame from an MJPEG stream (common in IP cameras, ESP32-CAM, Arduino cameras).

Parameters:

• mjpeg_url (str) - MJPEG stream URL (e.g., http://camera/video.mjpg)
• file_path (str) - Path where the image will be saved
• timeout_seconds (int, default: 10) - Connection timeout

Returns: Dictionary with status, file_path, and size_bytes

mjpeg_check_stream

Validates an MJPEG stream URL.

Parameters:

• mjpeg_url (str) - MJPEG stream URL to validate
• timeout_seconds (int, default: 10) - Connection timeout

Returns: Dictionary with status, url, and content_type

Screen Capture Tools

screen_list_monitors

Lists all available monitors/displays.

Returns: List of monitors with id, dimensions, and position

screen_save_image

Captures a full screenshot of a monitor.

Parameters:

• file_path (str) - Path where the image will be saved
• monitor (int, default: 0) - Monitor index (0 = all monitors combined)

Returns: Dictionary with status, file_path, and dimensions

screen_save_region

Captures a specific region of the screen.

Parameters:

• file_path (str) - Path where the image will be saved
• x (int) - X coordinate of top-left corner
• y (int) - Y coordinate of top-left corner
• width (int) - Width in pixels
• height (int) - Height in pixels

Returns: Dictionary with status, file_path, and region details

HTTP Image Tools

http_save_image

Downloads an image from a URL and saves it to disk.

Parameters:

• url (str) - Image URL (http:// or https://)
• file_path (str) - Path where the image will be saved
• timeout_seconds (int, default: 30) - Connection timeout

Returns: Dictionary with status, file_path, size_bytes, and content_type

http_check_image

Validates an image URL using a HEAD request.

Parameters:

• url (str) - Image URL to validate
• timeout_seconds (int, default: 10) - Connection timeout

Returns: Dictionary with status, content_type, and size_bytes

QR/Barcode Tools

Note: These tools require the libzbar system library. Install with: brew install zbar (macOS) or apt install libzbar0 (Linux)

decode_qr

Decodes QR codes from an image file.

Parameters:

• file_path (str) - Path to the image file

Returns: Dictionary with found, count, and codes list

decode_barcode

Decodes barcodes (EAN, UPC, Code128, etc.) from an image file.

Parameters:

• file_path (str) - Path to the image file

Returns: Dictionary with found, count, and codes list

decode_all

Decodes all QR codes and barcodes from an image file.

Parameters:

• file_path (str) - Path to the image file

Returns: Dictionary with found, count, and codes list

decode_and_annotate

Decodes codes and saves an annotated image with bounding boxes.

Parameters:

• file_path (str) - Path to the input image
• output_path (str) - Path for the annotated output image

Returns: Dictionary with found, count, output_path, and codes list

Image Analysis Tools

image_get_metadata

Extracts metadata from an image file including dimensions, format, and EXIF data.

Parameters:

• file_path (str) - Path to the image file

Returns: Dictionary with width, height, format, mode, file_size_bytes, and exif dict

{
  "width": 1920,
  "height": 1080,
  "format": "JPEG",
  "mode": "RGB",
  "file_size_bytes": 245678,
  "exif": {"Make": "Canon", "Model": "EOS R5", ...}
}

image_get_stats

Calculates basic image statistics including brightness, contrast, and sharpness.

Parameters:

• file_path (str) - Path to the image file

Returns: Dictionary with brightness (0-1), contrast (0-1), sharpness, and is_grayscale

{
  "brightness": 0.65,
  "contrast": 0.42,
  "sharpness": 2.35,
  "is_grayscale": false
}

image_get_histogram

Calculates color histogram for each channel (R, G, B) with optional visualization.

Parameters:

• file_path (str) - Path to the image file
• output_path (str, optional) - Path to save histogram visualization

Returns: Dictionary with channels (r, g, b arrays of 256 values) and output_path if provided

image_get_dominant_colors

Extracts dominant colors using K-means clustering.

Parameters:

• file_path (str) - Path to the image file
• num_colors (int, default: 5) - Number of colors to extract (1-20)

Returns: List of colors with RGB values, hex codes, and percentages

{
  "colors": [
    {"rgb": [64, 128, 192], "hex": "#4080C0", "percentage": 35.2},
    {"rgb": [255, 255, 255], "hex": "#FFFFFF", "percentage": 28.1}
  ]
}

Image Comparison Tools

image_compare_ssim

Compares two images using Structural Similarity Index (SSIM).

Parameters:

• file_path_1 (str) - Path to first image
• file_path_2 (str) - Path to second image
• threshold (float, default: 0.95) - Similarity threshold

Returns: Dictionary with ssim_score (-1 to 1), is_similar, and threshold

{
  "ssim_score": 0.9823,
  "is_similar": true,
  "threshold": 0.95
}

image_compare_mse

Compares two images using Mean Squared Error.

Parameters:

• file_path_1 (str) - Path to first image
• file_path_2 (str) - Path to second image

Returns: Dictionary with mse, is_identical, and normalized_mse (0-1)

image_compare_hash

Compares two images using perceptual hashing.

Parameters:

• file_path_1 (str) - Path to first image
• file_path_2 (str) - Path to second image
• hash_type (str, default: "phash") - Hash type: "phash", "dhash", or "ahash"

Returns: Dictionary with hash_1, hash_2, distance, is_similar, and hash_type

{
  "hash_1": "8f0f0f0f0f0f0f0f",
  "hash_2": "8f0f0f0f0f0f0f0f",
  "distance": 0,
  "is_similar": true,
  "hash_type": "phash"
}

image_get_hash

Generates a perceptual hash for a single image.

Parameters:

• file_path (str) - Path to the image file
• hash_type (str, default: "phash") - Hash type: "phash", "dhash", or "ahash"

Returns: Dictionary with hash (hex string) and hash_type

image_diff

Creates a visual diff highlighting differences between two images.

Parameters:

• file_path_1 (str) - Path to reference image
• file_path_2 (str) - Path to comparison image
• output_path (str) - Path to save diff visualization
• threshold (int, default: 30) - Pixel difference threshold (0-255)

Returns: Dictionary with status, output_path, diff_percentage, and diff_pixels

{
  "status": "success",
  "output_path": "/path/to/diff.png",
  "diff_percentage": 12.5,
  "diff_pixels": 25600
}

image_compare_histograms

Compares two images by their color histograms.

Parameters:

• file_path_1 (str) - Path to first image
• file_path_2 (str) - Path to second image
• method (str, default: "correlation") - Method: "correlation", "chi_square", "intersection", "bhattacharyya"

Returns: Dictionary with score, method, and is_similar

Detection Tools

detect_faces

Detects faces in an image using Haar cascades or DNN.

Parameters:

• file_path (str) - Path to the image file
• method (str, default: "haar") - Detection method: "haar" (fast) or "dnn" (accurate)

Returns: Dictionary with found, count, and faces list containing x, y, width, height, and confidence (DNN only)

{
  "found": true,
  "count": 2,
  "faces": [
    {"x": 120, "y": 80, "width": 150, "height": 150},
    {"x": 400, "y": 100, "width": 140, "height": 140, "confidence": 0.95}
  ]
}

detect_faces_save

Detects faces and saves an annotated image with bounding boxes.

Parameters:

• file_path (str) - Path to the input image
• output_path (str) - Path to save annotated image
• method (str, default: "haar") - Detection method: "haar" or "dnn"

Returns: Dictionary with found, count, output_path, and faces list

detect_motion

Compares two frames to detect motion between them.

Parameters:

• file_path_1 (str) - Path to the first (earlier) image
• file_path_2 (str) - Path to the second (later) image
• threshold (float, default: 25.0) - Pixel difference threshold (0-255)

Returns: Dictionary with motion_detected, motion_percentage, motion_regions list, and changed_pixels

{
  "motion_detected": true,
  "motion_percentage": 15.3,
  "motion_regions": [
    {"x": 200, "y": 150, "width": 80, "height": 120}
  ],
  "changed_pixels": 31250
}

detect_edges

Detects edges in an image using various methods.

Parameters:

• file_path (str) - Path to the input image
• output_path (str) - Path to save edge detection output
• method (str, default: "canny") - Method: "canny", "sobel", or "laplacian"

Returns: Dictionary with status, output_path, and method

{
  "status": "success",
  "output_path": "/path/to/edges.png",
  "method": "canny"
}

detect_objects

Detects common objects using MobileNet SSD.

Parameters:

• file_path (str) - Path to the image file
• confidence_threshold (float, default: 0.5) - Minimum confidence (0-1)

Returns: Dictionary with found, count, and objects list

Note: Requires pre-trained MobileNet SSD model files. Returns empty result if models are not available.

{
  "found": true,
  "count": 3,
  "objects": [
    {"class": "person", "confidence": 0.92, "x": 50, "y": 100, "width": 200, "height": 400},
    {"class": "car", "confidence": 0.87, "x": 300, "y": 250, "width": 180, "height": 120}
  ]
}

Technical Notes

OpenCV + MCP Compatibility

OpenCV prints debug messages to stderr which corrupts MCP's stdio communication. This server suppresses stderr at the file descriptor level before importing cv2 to prevent this issue.

Roadmap

• v0.1.0 - USB camera support via OpenCV
• v0.2.0 - IP camera support (RTSP and HLS streams)
• v0.3.0 - Multi-camera dashboard with realtime streaming
• v0.4.0 - Screen capture, MJPEG streams, HTTP images, QR/barcode decoding
• v0.5.0 - Image analysis and comparison tools (metadata, stats, SSIM, hashing, diff)
• v0.6.0 - Detection tools (face detection, motion detection, edge detection)
• v0.7.0 - Camera configuration (resolution, format, etc.)
• v0.8.0 - Video recording capabilities

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

License

MIT