AI Agent for OpenCV MCP Server
Seamlessly integrate OpenCV’s advanced computer vision capabilities into your AI workflows. The OpenCV MCP Server brings real-time image and video analysis, object detection, facial recognition, and more to your AI assistants and automation tools—all accessible via the Model Context Protocol (MCP). Empower your AI with robust image processing, statistical analysis, and video tracking for smarter, visually-aware solutions.
Powerful Computer Vision for AI Workflows
OpenCV MCP Server enables your AI systems to perform advanced image manipulation, real-time object and face detection, contour analysis, and more. Process images and videos in multiple formats, extract valuable insights, and automate visual tasks with ease.
- Smart Image Processing.
- Automate resizing, cropping, color space conversion, filtering, and statistical analysis of images for consistent and scalable results.
- Face and Object Detection.
- Leverage pre-configured DNN and YOLO models for robust face recognition and real-time object detection in images and videos.
- Video Frame Analysis.
- Extract frames, detect motion, track objects, and process live video for actionable insights and automation.
- Statistical Visual Insights.
- Access deep image and video statistics, histograms, and contour data to drive smarter AI decisions.
Advanced Vision Tools & Integration
Integrate advanced vision features into your AI workflows with tools for template matching, edge detection, feature extraction, and camera-based real-time analysis. Configure and extend with environment variables for full flexibility.
- Template & Feature Matching.
- Find templates and match keypoints between images for scene understanding and automation.
- Edge & Contour Detection.
- Detect edges, contours, and geometric shapes for precision analysis and visual data extraction.
- Flexible Configuration.
- Easily set up model directories, processing parameters, and camera sources for custom deployment.
Seamless Python & MCP Integration
Deploy in minutes with Python or integrate directly into Model Context Protocol-enabled environments like Claude Desktop. Full support for easy installation, environment variable configuration, and immediate access to OpenCV’s best-in-class vision tools.
- Easy Python API.
- Get started fast with Python scripting—resize images, apply filters, and run AI-powered vision tasks in just a few lines.
- MCP Protocol Ready.
- Plug into Model Context Protocol for seamless agent integration across popular AI assistants and platforms.
MCP INTEGRATION
Available OpenCV MCP Integration Tools
The following tools are available as part of the OpenCV MCP integration:
- save_image_tool
Save an image to a specified file path for persistent storage or further processing.
- convert_color_space_tool
Convert images between different color spaces such as BGR, RGB, GRAY, and HSV.
- resize_image_tool
Change the dimensions of an image to resize for various use cases.
- crop_image_tool
Extract a specific region from an image based on coordinates and size.
- get_image_stats_tool
Retrieve statistical information and histograms about an image's properties.
- apply_filter_tool
Apply different filters such as blur, gaussian, median, and bilateral to enhance or denoise images.
- detect_edges_tool
Detect edges in images using methods like Canny, Sobel, Laplacian, and Scharr.
- apply_threshold_tool
Apply thresholding techniques to images for segmentation or binarization.
- detect_contours_tool
Identify and optionally draw contours in images for shape and boundary detection.
- find_shapes_tool
Detect basic geometric shapes like circles and lines within images.
- match_template_tool
Locate a template image within a larger image to find matches.
- detect_features_tool
Detect feature points in images using SIFT, ORB, BRISK, and similar algorithms.
- match_features_tool
Match feature points between two images for comparison or alignment.
- detect_faces_tool
Detect human faces in images using Haar cascades or DNN-based models.
- detect_objects_tool
Detect general objects in images using deep neural network models like YOLO.
- extract_video_frames_tool
Extract individual frames from a video file based on frame selection parameters.
- detect_motion_tool
Detect motion by comparing differences between two video frames.
- track_object_tool
Track a specified object across video frames for movement analysis.
- combine_frames_to_video_tool
Assemble multiple image frames into a single video file.
- create_mp4_from_video_tool
Convert a video to MP4 format for compatibility and sharing.
- detect_video_objects_tool
Detect objects throughout a video and generate a results video.
- detect_camera_objects_tool
Detect objects from a live camera feed and save the annotated results as a video.
Bring Computer Vision to Your AI: Try OpenCV MCP Server Today
Empower your AI assistants with powerful image and video analysis using OpenCV MCP Server. Book a demo or get started for free and see advanced computer vision in action.
What is OpenCV MCP Server
OpenCV MCP Server is a Python package that brings OpenCV's robust image and video processing capabilities to the Model Context Protocol (MCP) ecosystem. Developed by GongRzhe, this server empowers AI assistants and applications to access a wide range of computer vision tools—from fundamental image manipulation (like reading, saving, and converting images) to advanced tasks such as real-time object detection, tracking, and face recognition. The server is open-source, written in Python, and designed to enable seamless integration with AI-powered applications, making it ideal for projects in autonomous systems, security, traffic analysis, augmented reality, and medical imaging.
Capabilities
What we can do with OpenCV MCP Server
The OpenCV MCP Server unlocks a broad set of computer vision capabilities for AI assistants and developers. With this service, you can process images and videos, detect and recognize objects in real time, and perform advanced analysis for a variety of industry applications.
- Basic Image Handling
- Read, save, and convert images easily through API calls.
- Advanced Image Processing
- Resize, crop, and apply filters to images for enhancement or transformation.
- Real-time Object Detection
- Detect and track objects in images and video streams as they happen.
- Video Analysis
- Extract frames, detect motion, and analyze video content for actionable insights.
- Face Detection & Recognition
- Identify and analyze faces for security, authentication, or interaction systems.
How AI Agents Benefit from OpenCV MCP Server
AI agents gain significant advantages by integrating with the OpenCV MCP Server. They can automate image and video analysis tasks, enhance their ability to perceive and understand visual content, and deliver smarter, context-aware responses in a wide range of real-world scenarios—from robotics to healthcare and security.