What is Mcp Server Cvdlt

mcp-server-cvdlt is a Python-based server implementing the Model Context Protocol (MCP) for various computer vision tasks, including object detection, segmentation, and pose estimation using deep learning models.

Use cases

Use cases include real-time object detection in surveillance systems, image segmentation for medical imaging analysis, human pose estimation in fitness applications, and automated quality inspection in manufacturing.

How to use

To use mcp-server-cvdlt, install the required dependencies using ‘uv sync’ and ‘uv pip install -r requirements.txt’. Start the server in stdio mode with ‘python server.py’ or in SSE mode with ‘python server.py sse [port]’. Ensure to download the necessary model weights into the ./checkpoints directory.

Key features

Key features include object detection using YOLOv10, image segmentation with YOLOv8, segmentation of entire images using Ultralytics SAM, human pose estimation with YOLOv8, and support for both local and network image inputs.

Where to use

mcp-server-cvdlt can be used in various fields such as robotics, autonomous vehicles, security surveillance, healthcare imaging, and any application requiring advanced image analysis.

MCP Server for CVDLT(Computer Vision & Deep Learning Tools)

The repo is based on Ultralytics and Model Context procotol of Python SDK
Related Links:

MCP Playground(client) - https://github.com/MRonaldo-gif/mcp-playground-local

Ultralytics - https://github.com/ultralytics/ultralytics

MCP of Python - https://github.com/modelcontextprotocol/python-sdk

Python server implementing Model Context Protocol (MCP) for image object detection, segmentation, and pose estimation operations.

Features

• Detect objects in images using YOLOv10
• Segment objects in images using YOLOv8
• Segment entire images using Ultralytics SAM
• Estimate human poses in images using YOLOv8
• Support for local and network image inputs
• MCP tool integration for client interactions
• Stdio and SSE transport protocols

Note: The server requires valid image paths or URLs and access to the following model files: yolov10b.pt (YOLOv10 detection), yolov8n-seg.pt (YOLOv8 segmentation), yolov8n-pose.pt (YOLOv8 pose estimation), and sam_b.pt (Ultralytics SAM).

TODO

• 3D Detection
• AIGC(GAN, Diffusion)
• Denso Estimation
• Deploy DL(Deep Learning) Models

QucikStart

Install Dependencies

uv sync
//如需要清华源
uv sync --index https://pypi.tuna.tsinghua.edu.cn/simple --extra-index-url https://pypi.org/simple

uv pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

Start Server

1. stdio 模式：

   python server.py
   

   输出：

   使用 stdio 传输启动 MCP 服务器（YOLO）
   

2. SSE 模式：

   python server.py sse [端口号]
   

   示例：

   python server.py sse 8080
   

   输出：

   在端口 8080 上启动 MCP 服务器（YOLO），使用 SSE 传输
   

Moreover, users need to download the weights into the ./checkpoints directory.
Downloads Links🔗：https://docs.ultralytics.com/models/yolov10/，https://docs.ultralytics.com/models/yolov8/，https://docs.ultralytics.com/models/sam-2/

├── checkpoints
│ ├── sam_b.pt
│ ├── yolov10b.pt
│ ├── yolov8n-pose.pt
│ └── yolov8n-seg.pt

API

Resources

• image://system: Image processing operations interface

Tools

• detect_objects
  • Detect objects in an image using YOLOv10
  • Input: image_url (string)
  • Supports local paths (file:// or relative) and network URLs (http:// or https://)
  • Returns JSON array of detected objects with bounding boxes, confidence scores, and class labels
  • Example output: [{"box": [x, y, w, h], "confidence": 0.9, "class": "person"}, ...]
• segment_objects
  • Segment objects in an image using YOLOv8
  • Input: image_url (string)
  • Supports local paths (file:// or relative) and network URLs (http:// or https://)
  • Returns JSON array of segmented objects with bounding boxes, confidence scores, and class labels
  • Example output: [{"box": [x, y, w, h], "confidence": 0.85, "class": "car"}, ...]
• segment_image
  • Segment entire image using Ultralytics SAM
  • Input: image_url (string)
  • Supports local paths (file:// or relative) and network URLs (http:// or https://)
  • Returns JSON array of segmented regions with bounding boxes, areas, and confidence scores
  • Example output: [{"bbox": [x, y, w, h], "area": 2500, "confidence": 0.95}, ...]
• estimate_pose
  • Estimate human poses in an image using YOLOv8
  • Input: image_url (string)
  • Supports local paths (file:// or relative) and network URLs (http:// or https://)
  • Returns JSON array of detected poses with keypoint coordinates and confidence scores
  • Example output: [{"keypoints": [[x1, y1], [x2, y2], ...], "confidence": [0.9, 0.8, ...]}, ...]

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

Note: You can provide sandboxed directories to the server by mounting them to /projects. Adding the ro flag will make the directory readonly by the server.

SSE

{
  "mcpServers": {
    "server-with-yolo": {
      "url": "http://localhost:8080/sse"
    }
  }
}