DINO-X
Advanced computer vision and object detection MCP server powered by Dino-X, enabling AI agents to analyze images, detect objects, identify keypoints, and perform visual understanding tasks.
التثبيت
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التوثيق
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المحتويات
DINO-X MCP Server
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DINO-X Official MCP Server — powered by the DINO-X and Grounding DINO models — brings fine-grained object detection and image understanding to your multimodal applications.
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Why DINO-X MCP?
With DINO-X MCP, you can:
- Fine-Grained Understanding: Full image detection, object detection, and region-level descriptions.
- Structured Outputs: Get object categories, counts, locations, and attributes for VQA and multi-step reasoning tasks.
- Composable: Works seamlessly with other MCP servers to build end-to-end visual agents or automation pipelines.
Transport Modes
DINO-X MCP supports two transport modes:
| Feature | STDIO (default) | Streamable HTTP |
|---|---|---|
| Runtime | Local | Local or Cloud |
| Transport | Standard I/O | HTTP (streaming responses) |
| Input source | file:// and https:// | https:// only |
| Visualization | Supported (saves annotated images locally) | Not supported (for now) |
Quick Start
1. Prepare an MCP client
Any MCP-compatible client works, e.g.:
2. Get your API key
Apply on the DINO-X platform: Request API Key (new users get free quota).
3. Configure MCP
Option A: Official Hosted Streamable HTTP (Recommended)
Add to your MCP client config and replace with your API key:
{
"mcpServers": {
"dinox-mcp": {
"url": "https://mcp.deepdataspace.com/mcp?key=your-api-key"
}
}
}
Option B: Use the NPM package locally (STDIO)
Install Node.js first
- Download the installer from nodejs.org
- Or use command:
# macOS / Linux
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
# load nvm into current shell (choose the one you use)
source ~/.bashrc || true
source ~/.zshrc || true
# install and use LTS Node.js
nvm install --lts
nvm use --lts
# Windows (one of the following)
winget install OpenJS.NodeJS.LTS
# or with Chocolatey (in admin PowerShell)
iwr -useb https://raw.githubusercontent.com/chocolatey/chocolatey/master/chocolateyInstall/InstallChocolatey.ps1 | iex
choco install nodejs-lts -y
Configure 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"
}
}
}
}
Note: Replace your-api-key-here with your real key.
Option C: Run from source locally
Make sure Node.js is installed (see Option B), then:
# clone
git clone https://github.com/IDEA-Research/DINO-X-MCP.git
cd DINO-X-MCP
# install deps
npm install
# build
npm run build
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"
}
}
}
}
CLI Flags & Environment Variables
- Common flags
--http: start in Streamable HTTP mode (otherwise STDIO by default)--stdio: force STDIO mode--dinox-api-key=...: set API key--enable-client-key: allow API key via URL?key=(Streamable HTTP only)--port=8080: HTTP port (default 3020)
Environment variables
DINOX_API_KEY(required/conditionally required): DINO-X platform API keyIMAGE_STORAGE_DIRECTORY(optional, STDIO): directory to save annotated imagesAUTH_TOKEN(optional, HTTP): if set, client must sendAuthorization: Bearer <token>
Examples:
# STDIO (local)
node build/index.js --dinox-api-key=your-api-key
# Streamable HTTP (server provides a shared API key)
node build/index.js --http --dinox-api-key=your-api-key
# Streamable HTTP (custom port)
node build/index.js --http --dinox-api-key=your-api-key --port=8080
# Streamable HTTP (require client-provided API key via URL)
node build/index.js --http --enable-client-key
Client config when using ?key=:
{
"mcpServers": {
"dinox-mcp": {
"url": "http://localhost:3020/mcp?key=your-api-key"
}
}
}
Using AUTH_TOKEN with a gateway that injects Authorization: Bearer <token>:
AUTH_TOKEN=my-token node build/index.js --http --enable-client-key
Client example with supergateway:
{
"mcpServers": {
"dinox-mcp": {
"command": "npx",
"args": [
"-y",
"supergateway",
"--streamableHttp",
"http://localhost:3020/mcp?key=your-api-key",
"--oauth2Bearer",
"my-token"
]
}
}
}
Tools
| Capability | Tool ID | Transport | Input | Output |
|---|---|---|---|---|
| Full-scene object detection | detect-all-objects | STDIO / HTTP | Image URL | Category + bbox + (optional) captions |
| Text-prompted object detection | detect-objects-by-text | STDIO / HTTP | Image URL + English nouns (dot-separated for multiple, e.g., person.car) | Target object bbox + (optional) captions |
| Human pose estimation | detect-human-pose-keypoints | STDIO / HTTP | Image URL | 17 keypoints + bbox + (optional) captions |
| Visualization | visualize-detection-result | STDIO only | Image URL + detection results array | Local path to annotated image |
🎬 Use Cases
| 🎯 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:
||
FAQ
- Supported image sources?
- STDIO:
file://andhttps:// - Streamable HTTP:
https://only
- STDIO:
- Supported image formats?
- jpg, jpeg, webp, png
Development & Debugging
Use watch mode to auto-rebuild during development:
npm run watch
Use MCP Inspector for debugging:
npm run inspector
License
Apache License 2.0
الأسئلة الشائعة
ما هو DINO-X؟
Advanced computer vision and object detection MCP server powered by Dino-X, enabling AI agents to analyze images, detect objects, identify keypoints, and perform visual understanding tasks.
كيف أثبّت DINO-X؟
اتبع خطوات التثبيت في توثيقه المعروض في هذه الصفحة وفي مستودعه.
هل DINO-X مفتوح المصدر؟
نعم. شيفرته متاحة على GitHub بترخيص Apache-2.0.
من يصون DINO-X؟
ينشره IDEA-Research. يدرجه AI.tn لكنه لا يصونه ولا يوصي به.
من القوائم المجتمعية awesome-mcp-servers ومستودع المشروع على GitHub. الإدراج ليس توصية: راجع شيفرة الخادم وصلاحياته قبل منحه الوصول إلى بياناتك.