Provides visualization capabilities for object detection results, allowing bounding boxes, keypoints, and other visual markers to be overlaid on the original image for better presentation of analysis results.
Enables running the DINO-X MCP server, which provides tools for fine-grained object detection and image understanding in AI applications.
Used as the package manager for installing and building the DINO-X MCP server project.
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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.
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.
| 🎯 Scenario | 📝 Input | ✨ Output |
|---|---|---|
| Detection & Localization | 💬 Prompt:Detect the fire areas in the forest and visualize with Canvas🖼️ Input Image: | |
| Object Counting | 💬 Prompt:Please analyze this warehouse image, detect all the cardboard boxes, count the total number, and create a complete Canvas visualization webpage.🖼️ Input Image: | |
| Feature Detection | 💬 Prompt:Find all red cars in the image and visualize with Canvas🖼️ Input Image: | |
| Attribute Reasoning | 💬 Prompt:Find the tallest person in the image, describe their clothing, and visualize the result with Canvas🖼️ 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 and overlay the keypoints on the original image using canvas🖼️ Input Image: |
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:
You can use DINO-X MCP server in two ways:
Add the following configuration in your MCP client:
First, clone and build the project:
Then configure your MCP client:
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.
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 |
https:// 👍file://)jpg, jpeg, png, webpPlease refer to DINO-X Platform for API usage limits and pricing information.
During development, you can use watch mode for automatic rebuilding:
Use MCP Inspector to debug the server:
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Empower LLMs with fine-grained visual understanding — detect, localize, and describe anything in images with natural language prompts.
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