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Dino X 图像检测 (image Detection) MCP Server

@IDEA-Research

Repository

Basic information

Category

Media & Design

Transports

stdio

Author

IDEA-Research

Submitted by

Ce Fung

About Dino X 图像检测 (image Detection) MCP Server

No overview available yet

#dino-x#image-detection#object-detection

Dino X 图像检测 (image Detection) MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "dinox-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@deepdataspace/dinox-mcp"
      ],
      "env": {
        "DINOX_API_KEY": "Get free api key from https://cloud.deepdataspace.com/request_api",
        "IMAGE_STORAGE_DIRECTORY": "/path/to/your/image/directory"
      }
    }
  }
}

Dino X 图像检测 (image Detection) MCP Server tools

4

Analyze an image based on a text prompt to identify and count specific objects, and return detailed descriptions of the objects and their 2D coordinates.

Analyze an image to detect all identifiable objects, returning the category, count, coordinate positions and detailed descriptions for each object.

Detects 17 keypoints for each person in an image, supporting body posture and movement analysis.

Visualize detection results by drawing bounding boxes and labels on the original image. Images are saved to the directory specified by IMAGE_STORAGE_DIRECTORY environment variable.

Overview

What is Dino X 图像检测 (image Detection)?

Dino X 图像检测 (image Detection) is an MCP server that enables large language models to perform fine-grained object detection and image understanding. It is powered by the DINO-X and Grounding DINO 1.6 API, providing precise localization, object counting, attribute reasoning, and structured visual outputs. This server is designed for developers and AI assistants who need to integrate structured visual analysis into natural language workflows.

How to use Dino X 图像检测 (image Detection)?

To use the server, install Node.js and configure an MCP client (e.g., Cursor, WindSurf, Cherry Studio) with the server’s NPM package or a local build. Set the required environment variable DINOX_API_KEY (obtain from the DINO-X Platform) and optionally IMAGE_STORAGE_DIRECTORY. After restarting the client, the server exposes four tools: detect-all-objects, object-detection-by-text, detect-human-pose-keypoints, and visualize-detections.

Key features of Dino X 图像检测 (image Detection)

  • Fine-grained image understanding with full-scene recognition.
  • Targeted object detection based on natural language prompts.
  • Object counting, localization, and bounding box output.
  • Human pose keypoint detection (17 keypoints per person).
  • Visualizes detection results by drawing annotations on images.

Use cases of Dino X 图像检测 (image Detection)

  • Detection & localization of objects (e.g., fire areas in a forest).
  • Counting objects in warehouse or inventory images.
  • Finding specific features (e.g., all red cars in an image).
  • Attribute reasoning (e.g., describing the clothing of the tallest person).
  • Full scene detection with semantic understanding (e.g., identifying fruits high in vitamin C).

Frequently asked questions

1

What API key is required and how to get it?

You need a DINO-X API key, which can be obtained from the DINO-X Platform. A free quota is available for new users.

2

What image formats and sources are supported?

Supported image formats are jpg, jpeg, png, and webp. Images can be provided as remote URLs (starting with `https://`) or local file paths (starting with `file://`).

3

What are the runtime dependencies?

The server requires Node.js (LTS recommended) and an MCP client such as Cursor, WindSurf, Trae, or Cherry Studio.

4

Where are visualization images saved?

By default, annotated images are saved to `/tmp/dinox-mcp` on macOS/Linux or `%TEMP%\dinox-mcp` on Windows. You can customize this path with the `IMAGE_STORAGE_DIRECTORY` environment variable.

5

Are there usage limits or pricing?

Usage limits and pricing are managed by the DINO-X API. Please refer to the DINO-X Platform API documentation for details.

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