What is Dino X Mcp

DINO-X MCP is a powerful tool that enables large language models to perform fine-grained object detection and image understanding, leveraging DINO-X and the Grounding DINO 1.6 API. It aims to improve the accuracy and detail of visual content analysis beyond standard multimodal capabilities.

Use cases

Some notable use cases for DINO-X MCP include detecting and localizing objects in images, counting specific items, analyzing attributes and features of objects, reasoning about image content like identifying the tallest person, and conducting full scene detection. It supports advanced tasks such as visual question answering and pose analysis.

How to use

To use DINO-X MCP, first install Node.js and set up an MCP client with the provided configuration. You can either utilize the NPM package or clone the project locally. Once configured with an API key from the DINO-X Platform, you can access various methods for image analysis by restarting your MCP client.

Key features

Key features of DINO-X MCP include fine-grained image understanding, accurate object detection and localization, the ability to process natural language prompts for targeted image analysis, integration capability with other MCP servers for complex workflows, and support for various input image formats.

Where to use

DINO-X MCP can be used in various applications, especially those requiring advanced visual understanding such as AI assistants, automation systems, and image analysis tools in fields like logistics, environmental monitoring, and social media analysis, among others.

DINO-X MCP

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Enables large language models to perform fine-grained object detection and image understanding, powered by DINO-X and Grounding DINO 1.6 API.

💡 Why DINO-X MCP?

Although multimodal models can understand and describe images, they often lack precise localization and high-quality structured outputs for visual content.

With DINO-X MCP, you can:

🧠 Achieve fine-grained image understanding — both full-scene recognition and targeted detection based on natural language.

🎯 Accurately obtain object count, position, and attributes, enabling tasks such as visual question answering.

🧩 Integrate with other MCP Servers to build multi-step visual workflows.

🛠️ Build natural language-driven visual agents for real-world automation scenarios.

🎬 Use Case

🎯 Scenario	📝 Input	✨ Output
Detection & Localization	💬 Prompt: Detect and visualize the fire areas in the forest 🖼️ Input Image:
Object Counting	💬 Prompt: Please analyze this warehouse image, detect all the cardboard boxes, count the total number 🖼️ Input Image:
Feature Detection	💬 Prompt: Find all red cars in the image 🖼️ Input Image:
Attribute Reasoning	💬 Prompt: Find the tallest person in the image, describe their clothing 🖼️ Input Image:
Full Scene Detection	💬 Prompt: Find the fruit with the highest vitamin C content in the image 🖼️ Input Image:	Answer: Kiwi fruit (93mg/100g)
Pose Analysis	💬 Prompt: Please analyze what yoga pose this is 🖼️ Input Image:

🚀 Quick Start

1. Prerequisites

You can install Node.js using one of the following methods:

Option A: Command 👍

# For MacOS or Linux
# 1. Install nvm (Node Version Manager)
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash
# OR
wget -qO- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash

# 2. Add these lines to your profile (~/.bash_profile, ~/.zshrc, ~/.profile, or ~/.bashrc)
export NVM_DIR="$HOME/.nvm"
[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"  
[ -s "$NVM_DIR/bash_completion" ] && \. "$NVM_DIR/bash_completion"  

# 3. Activate nvm in current shell
source ~/.bashrc
# Or
source ~/.zshrc   

# 4. Verify nvm installation
command -v nvm

# 5. Install and use LTS version of Node.js
nvm install --lts
nvm use --lts

# For Windows
winget install OpenJS.NodeJS.LTS
# Or using PowerShell (Administrator)
iwr -useb https://raw.githubusercontent.com/chocolatey/chocolatey/master/chocolateyInstall/InstallChocolatey.ps1 | iex
choco install nodejs-lts -y

Option B: Manual Installation

Download the installer from nodejs.org

Also, choose an AI assistants and applications that support the MCP Client, including but not limited to:

• Cursor
• WindSurf
• Trae
• Cherry Studio

2. Configure MCP Sever

You can use DINO-X MCP server in two ways:

Option A: Using NPM Package 👍

Add the following configuration in your MCP client:

{
  "mcpServers": {
    "dinox-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@deepdataspace/dinox-mcp"
      ],
      "env": {
        "DINOX_API_KEY": "your-api-key-here",
        "IMAGE_STORAGE_DIRECTORY": "/path/to/your/image/directory"
      }
    }
  }
}

Option B: Using Local Project

First, clone and build the project:

# Clone the project
git clone https://github.com/IDEA-Research/DINO-X-MCP.git
cd DINO-X-MCP

# Install dependencies
pnpm install

# Build the project
pnpm run build

Then configure your MCP client:

{
  "mcpServers": {
    "dinox-mcp": {
      "command": "node",
      "args": [
        "/path/to/DINO-X-MCP/build/index.js"
      ],
      "env": {
        "DINOX_API_KEY": "your-api-key-here",
        "IMAGE_STORAGE_DIRECTORY": "/path/to/your/image/directory"
      }
    }
  }
}

3. Get API Key

Get your API key from DINO-X Platform (A free quota is available for new users).

Replace your-api-key-here in the configuration above with your actual API key.

4. Environment Variables

The DINO-X MCP server supports the following environment variables:

5. Available Tools

Restart your MCP client, and you should be able to use the following tools:

📝 Usage

Supported Image Formats

• Remote URLs starting with https:// 👍
• Local file paths (starting with file://)
• Common image formats: jpg, jpeg, png, webp

API Docs

Please refer to DINO-X Platform for API usage limits and pricing information.

🛠️ Development

Watch Mode

During development, you can use watch mode for automatic rebuilding:

pnpm run watch

Debugging

Use MCP Inspector to debug the server:

pnpm run inspector

License

Apache License 2.0