DataRobot Connector for AI agents.
6 live capabilities
Manage your AutoML lifecycle and model deployments from a single chat.
Waiting for input…
Why people use DataRobot
DataRobot for Automating ML Lifecycle Management
DataRobot MCP changes that by bringing your AutoML workspace into your chat. You can stay in your flow and just ask your agent to pull project details, check model health, or list your active deployments. You get the information you need in seconds without ever leaving your workspace.
What Vinkius changes
You get a direct line to your DataRobot workspace without leaving your chat window.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Checking a model's validation score
A data scientist asks the agent to show the top models for a specific project.
- Real-world use case 02
Verifying production health
An ML engineer needs to know if the pricing engine is still live.
- Real-world use case 03
Auditing dataset usage
A platform lead wants to see which datasets are being used in the sales project.
Complete set · 6capabilities
The complete DataRobot capability set.
These are the exact actions your AI can choose when you ask it to work with DataRobot.
01—03
3 capabilities in this set.
Part of 6 available through DataRobot.
- 01 Capability
Get model
Get specific model properties. Use this to pull raw training metrics and logical properties for a specific model.
- 02 Capability
List deployments
List all active deployments. This shows you exactly where your models are running and if they are healthy.
- 03 Capability
List datasets
List all datasets. Use this to inspect the data being used across your various ML projects.
04—06
3 capabilities in this set.
Part of 6 available through DataRobot.
- 04 Capability
List projects
List all projects in your DataRobot workspace. This helps you see your project boundaries at a glance.
- 05 Capability
Get project
Get details for a specific project. Use this to see the nested elements and configurations of a single project.
- 06 Capability
List models
List all models within a project. This is the fastest way to see what models are available for use.
Set up in minutes
One URL. Then ask DataRobot to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use DataRobot from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_WX1YGmPzxHEF3itbazDacfMS9v73zZdwIlCWtf4z/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it DataRobot, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable DataRobot for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_WX1YGmPzxHEF3itbazDacfMS9v73zZdwIlCWtf4z/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the DataRobot URL.
- Step 03
Save and start
Save the connection and enable DataRobot in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"datarobot": {
"url": "https://edge.vinkius.com/vk_preview_WX1YGmPzxHEF3itbazDacfMS9v73zZdwIlCWtf4z/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using DataRobot
Open Agent mode in chat and ask: "Using DataRobot, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"datarobot": {
"url": "https://edge.vinkius.com/vk_preview_WX1YGmPzxHEF3itbazDacfMS9v73zZdwIlCWtf4z/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using DataRobot
Ask Copilot: "Using DataRobot, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"datarobot": {
"url": "https://edge.vinkius.com/vk_preview_WX1YGmPzxHEF3itbazDacfMS9v73zZdwIlCWtf4z/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using DataRobot
Open Cascade and ask: "Using DataRobot, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"datarobot": {
"url": "https://edge.vinkius.com/vk_preview_WX1YGmPzxHEF3itbazDacfMS9v73zZdwIlCWtf4z/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using DataRobot
Ask Cline: "Using DataRobot, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add datarobot --transport http "https://edge.vinkius.com/vk_preview_WX1YGmPzxHEF3itbazDacfMS9v73zZdwIlCWtf4z/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using DataRobot
Ask Claude: "Using DataRobot, show me...". 6 tools are ready
Where the request belongs
Work DataRobot can move forward.
This is for data professionals who are tired of clicking through multiple dashboards to find a single model metric or deployment status. It's built for anyone managing complex AutoML workflows.
Data Scientist
Use this to quickly pull training metrics and compare model performance across different projects during research.
ML Engineer
Audit production deployments and verify AI configurations in real-time using natural language prompts.
Data Platform Team
Monitor project-wide dataset usage and model metadata across the entire organization.
AI Researcher
Retrieve discrete properties from experiment models quickly during the prototyping phase.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsH2O.ai
Manage AI models via H2O.ai. track data frames, monitor machine learning models and training jobs, and audit cloud cluster status directly from any AI agent.
Arize AI
Monitor ML model performance, detect data drift, and troubleshoot prediction quality with real-time observability dashboards.
Abacus AI (Enterprise AI Cloud)
Manage the full machine learning lifecycle via Abacus AI. create projects, train models, and deploy real-time prediction endpoints.
MindsDB (AI Database & Predictors)
Manage AI-powered data via MindsDB. execute SQL predictions, audit ML models, and connect data sources.
Modelbit (ML Model Deployments)
Deploy and call machine learning models directly from your AI agent using Modelbit's inference endpoints.
Comet ML
Manage machine learning experiments via Comet. track model metrics, audit project workspaces, and inspect ML run parameters directly from any AI agent.
Bring your own AI
Change the model, client or framework. Keep DataRobot connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about DataRobot.
The practical details behind the request, access and result.
Can I use the DataRobot MCP with Claude or Cursor?
Yes, this Connector works with any AI client that supports the Model Context Protocol, including Claude, Cursor, and Windsurf. Once connected, you can manage your ML projects directly through those apps.
How do I connect DataRobot to my AI agent?
You just need to subscribe to the Connector and provide your DataRobot API Key and Endpoint URL. You can find these in your DataRobot profile settings.
Can the DataRobot MCP monitor my model deployments?
Yes, it can. Your agent can pull a list of all active deployments and report on their current health status so you don't have to check the dashboard manually.
Does the DataRobot MCP show dataset metrics?
It does. You can ask your agent to list the datasets being used in your projects and retrieve specific metrics about the data being extracted.
Is this DataRobot MCP for Data Scientists?
It's perfect for data scientists, ML engineers, and data platform teams. It's designed to help those roles manage the ML lifecycle and audit model performance more efficiently.
Can I pull training metrics for specific models?
Yes, the Connector allows your agent to retrieve discrete logical properties and raw training metrics for specific models, making it easier to compare performance during the research phase.
Can my agent list all models within a specific DataRobot project?
Yes. Use the 'list_models' capability and provide the project ID. The agent will enumerate the explicit bounded layers and AI configurations stored directly in the DataRobot platform, allowing you to compare models through the chat.
How do I retrieve training metrics for a specific model via chat?
Provide the project ID and model ID to the 'get_model' capability. Your agent will retrieve the discrete logical properties and natively export raw training metrics within your mapped ML structures accurately.
Can I monitor active cloud deployments through the agent?
Absolutely. Use the 'list_deployments' capability. Your agent will intercept precise global configurations tracing executed DataRobot nodes deployed natively into scalable clouds, giving you real-time visibility into your production AI.
One connection away
Give your agent a direct line to DataRobot.
Connect DataRobot once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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