How to Use the Dataiku DSS MCP in Cursor

Q: How do I install the Dataiku DSS integration in Cursor?

Create an mcp.json file in your project root or global .cursor directory. Add the server configuration under the mcpServers key. Switch to Agent mode in the chat to start using the tools.

Bring Dataiku DSS metadata into Cursor to write code against real datasets, recipes, and ML models.

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Connect Dataiku DSS MCP to Cursor

Create your Vinkius account to connect Dataiku DSS to Cursor and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Dataiku DSS with Cursor

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Code against live Dataiku DSS schemas

Writing Python scripts to interact with Dataiku usually involves guessing column names or switching tabs to check the web UI. Cursor changes that by pulling the exact structure into your editor. The agent runs `list_datasets` to find your target table. Then it hits `dataset_schema` to grab the exact column names and data types. Cursor uses this live context to write accurate pandas transformations or SQL queries that match your real environment on the first try.

Read model metrics via this MCP Server

You need to know how a model performs before you write an API integration for it. Cursor calls `list_models` to find deployed algorithms in your project. To get the specifics, the agent fires `get_model`. It pulls back the algorithm type and performance metrics directly into your chat panel. You write your inference code based on actual model parameters instead of outdated documentation.

Map out project infrastructure in Cursor

Understanding how a project is wired together is critical when taking over an existing codebase. You ask Cursor to map the dependencies. It triggers `list_connections` to show you which databases and APIs the project relies on. Next, the agent uses `list_recipes` and `get_recipe` to pull the configuration for specific data transformations. You see exactly how the data moves through the pipeline, making it easier to debug or extend the current logic.

Setup guide

Set up Dataiku DSS MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Dataiku DSS MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Dataiku DSS transactions." If the MCP tools are loaded correctly, Cursor will call the Dataiku DSS tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "dataiku-dss-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Dataiku. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Dataiku DSS MCP in Cursor

Create an `mcp.json` file in your project root or global `.cursor` directory. Add the server configuration under the `mcpServers` key. Switch to Agent mode in the chat to start using the tools.

Yes. If you have an automation scenario set up, your agent can execute it. It calls `run_scenario` to kick off the pipeline directly from your editor.

The server only exposes metadata. Your agent reads project settings, recipe configurations, and column schemas, but it does not extract the raw rows from your datasets.

Ask your agent to check the status of a recent run. It will use `list_jobs` to find the execution ID, then call `get_job` to pull the timing details and output state.

Vinkius runs the MCP Server in an ephemeral, zero-trust sandbox. Cursor only accesses structural data like recipe settings, model algorithms, and connection names. Your underlying database credentials and raw records remain entirely within your infrastructure.

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