DINO-X Image Detection MCP Server

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 the fire areasin the forest and visualizewith Canvas`🖼️ Input Image:
Object Counting	💬 Prompt:`Please analyze thiswarehouse image, detectall the cardboard boxes,count the total number`🖼️ Input Image:
Feature Detection	💬 Prompt:`Find all red carsin the image`🖼️ Input Image:
Attribute Reasoning	💬 Prompt:`Find the tallest personin the image, describetheir clothing`🖼️ Input Image:
Full Scene Detection	💬 Prompt:`Find the fruit withthe highest vitamin Ccontent in the image`🖼️ Input Image:	Answer: Kiwi fruit (93mg/100g)
Pose Analysis	💬 Prompt:`Please analyze whatyoga pose this is`🖼️ Input Image:

🚀 Quick Start

1. Prerequisites

Make sure you have Node.js installed. If you don't have Node.js, download it from nodejs.org.

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

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"
      }
    }
  }
}

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"
      }
    }
  }
}

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. Available Tools

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

Method Name	Description	Input	Output
`detect-all-objects`	Detects and localizes all recognizable objects in an image.	Image	Category names + bounding boxes + captions
`object-detection-by-text`	Detects and localizes objects in an image based on a natural language prompt.	Image + Text prompt	Bounding boxes + object captions
`detect-human-pose-keypoints`	Detects 17 human body keypoints per person in an image for pose estimation.	Image	Keypoint coordinates and captions

📝 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

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Empower LLMs with fine-grained visual understanding — detect, localize, and describe anything in images with natural language prompts.

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