by IDEA-Research

English | δΈζ
DINO-X Official MCP Server β powered by the DINO-X and Grounding DINO models β brings fine-grained object detection and image understanding to your multimodal applications.
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:
*Configuration content*
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:
*Configuration content*
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:
*Configuration content*
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=
:
*Configuration content*
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
:
*Configuration content*
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 |
π― Scenario | π Input | β¨ Output |
---|---|---|
Detection & Localization | π¬ Prompt:Detect and visualize the fire areas in the forest πΌοΈ Input Image: | |
Object Counting | π¬ Prompt:Please analyze this warehouse image, detect all the cardboard boxes, count the total number πΌοΈ Input Image: | |
Feature Detection | π¬ Prompt:Find all red cars in the image πΌοΈ Input Image: | |
Attribute Reasoning | π¬ Prompt:Find the tallest person in the image, describe their clothing πΌοΈ Input Image: | |
Full Scene Detection | π¬ Prompt:Find the fruit with the highest vitamin C content in the image πΌοΈ Input Image: | Answer: Kiwi fruit (93mg/100g) |
Pose Analysis | π¬ Prompt:Please analyze what yoga pose this is πΌοΈ Input Image: |
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
No version information available