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.
⚡Config Installation
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"dino-x": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-dino-x"
]
}
}
}* Note: Requires restart of Claude Desktop app.
Deployment Infrastructure
Adoption Framework for DINO-X
Before installing any skill, define a clear objective and measurable outcome. A useful implementation question is: what workflow becomes faster, safer, or more reliable after this skill is active? If that answer is vague, delay rollout and tighten scope first.
For most teams, a low-risk pattern is preview-first rollout with one owner, one test scenario, and one rollback plan. Capture failures in a structured log so quality decisions are evidence-based. This is especially important for skills that touch file systems, external APIs, or automation chains with downstream side effects.
- Define success metrics before installation.
- Validate permission scope against policy boundaries.
- Run one controlled pilot and document failure categories.
- Promote only after acceptance checks pass consistently.
Pre-Deployment Review Questions
Use these questions before enabling the skill in shared environments. They reduce surprise incidents and make approval decisions consistent across teams.
- What data can this skill read, write, or transmit by default?
- Which failures are recoverable automatically and which require manual stop?
- Do we have verifiable logs that prove safe behavior under load?
- Is rollback tested, documented, and assigned to a clear owner?
If any answer is unclear, keep rollout in preview and close the gap before production use.
Editorial Review Snapshot
This listing includes an editorial QA layer in addition to automated rendering. Review status is based on documentation depth, content uniqueness, and operational safety signals from the upstream repository.
- Last scan date: 2026-01-18
- README depth: 915 words
- Content diversity score: 0.60 (higher is better)
- Template signal count: 0
- Index status: Index eligible
Recommendation: Pilot in a bounded environment first. Confirm observability and ownership before promoting to shared workflows.
Skill Implementation Board
Actionable utility module for rollout decisions. Use the inputs below to choose a deployment path, then execute the checklist and record an output note.
Input: Security Grade
B
Input: Findings
0
Input: README Depth
915 words
Input: Index State
Eligible
| Decision Trigger | Action | Expected Output |
|---|---|---|
| Input: risk band moderate, docs partial, findings 0 | Run a preview pilot with fixed ownership and observability checkpoints. | Pilot can start with rollback checklist attached. |
| Input: page is index-eligible | Proceed with external documentation and team onboarding draft. | Reusable rollout runbook ready for team adoption. |
| Input: context tags/scenarios are missing | Define two concrete scenarios before broad rollout. | Clear scope definition before further deployment. |
Execution Steps
- Capture objective, owner, and rollback contact.
- Run one preview pilot with fixed test scenario.
- Record warning behavior and recovery evidence.
- Promote only if pilot output matches expected threshold.
Output Template
skill=dino-x mode=B pilot_result=pass|fail warning_count=0 next_step=rollout|patch|hold
🛡️ Security Analysis
Clean Scan Report
Our static analysis engine detected no common vulnerabilities (RCE, API Leaks, Unbounded FS).
DocumentationREADME.md
DINO-X MCP Server
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.
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 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: |
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