by idea-research
DINO-X is a powerful multimodal AI model that lets you detect, localize, and describe anything in images using natural l
Provides AI-powered object detection and visual analysis in images using natural language prompts. Works with local files or web URLs to find, locate, and describe specific objects or regions.
DINO-X is a community-built MCP server published by idea-research that provides AI assistants with tools and capabilities via the Model Context Protocol. DINO-X is a powerful multimodal AI model that lets you detect, localize, and describe anything in images using natural l It is categorized under ai ml.
You can install DINO-X in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
Apache-2.0
DINO-X is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
DINO-X is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
DINO-X is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
DINO-X reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Useful MCP listing: DINO-X is the kind of server we cite when onboarding engineers to host + tool permissions.
We wired DINO-X into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
DINO-X is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Strong directory entry: DINO-X surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Strong directory entry: DINO-X surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Useful MCP listing: DINO-X is the kind of server we cite when onboarding engineers to host + tool permissions.
DINO-X reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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English | 中文
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.
<p align="center"> <video width="800" controls> <source src="https://dds-frontend.oss-cn-shenzhen.aliyuncs.com/dinox-mcp/dinox-mcp-en-overveiw.mp4" type="video/mp4"> Your browser does not support the video tag. </video> </p>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.
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) |
Any MCP-compatible client works, e.g.:
Apply on the DINO-X platform: Request API Key (new users get free quota).
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"
}
}
}
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.
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"
}
}
}
}
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 send Authorization: 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"
]
}
}
}
| 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 |
| 🎯 Scenario | 📝 Input | ✨ Output |
|---|---|---|
| Detection & Localization | 💬 Prompt:<br>Detect and visualize the <br>fire areas in the forest <br><br>🖼️ Input Image:<br> | |
| Object Counting | 💬 Prompt:<br>Please analyze this<br>warehouse image, detect<br>all the cardboard boxes,<br>count the total number<br><br>🖼️ Input Image:<br> | <img width="1276" alt="2-2" src="https://github.com/user-attachments/assets/3f18ef8c-5e89-45fc-bd0f-f23381304272" /> |
| Feature Detection | 💬 Prompt:<br>Find all red cars<br>in the image<br><br>🖼️ Input Image:<br> | |
| Attribute Reasoning | 💬 Prompt:<br>Find the tallest person<br>in the image, describe<br>their clothing<br><br>🖼️ Input Image:<br> | |
| Full Scene Detection | 💬 Prompt:<br>Find the fruit with<br>the highest vitamin C<br>content in the image<br><br>🖼️ Input Image:<br> | |
| Pose Analysis | 💬 Prompt:<br>Please analyze what<br>yoga pose this is<br><br>🖼️ Input Image:<br> |
file:// and https://https:// onlyUse watch mode to auto-rebuild during development:
npm run watch
Use MCP Inspector for debugging:
npm run inspector
Apache License 2.0
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.