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Use Dataiku with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Manage data science via Dataiku. list projects and datasets, track pipeline jobs, run automation scenarios, and monitor ML models directly from any AI agent.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 14 capabilities

The complete Dataiku capability set.

These are the exact actions your AI can choose when you ask it to work with Dataiku.

Capability set01 / 04

01-04

4 capabilities in this set.

Part of 14 available through Dataiku.

  1. 01

    List jobs

    List pipeline jobs in a project (build tasks, training runs)

  2. 02

    Get recipe

    Get recipe configuration and settings

  3. 03

    Get job

    Get job state, timing, and outputs

  4. 04

    Get project

    Get project metadata, settings, and tags

Capability set02 / 04

05-08

4 capabilities in this set.

Part of 14 available through Dataiku.

  1. 05

    List connections

    List all DSS data connections (databases, cloud storage, APIs)

  2. 06

    List datasets

    List all datasets in a project

  3. 07

    List recipes

    List all recipes (data transformations) in a project

  4. 08

    List scenarios

    List automation scenarios in a project

Capability set03 / 04

09-11

3 capabilities in this set.

Part of 14 available through Dataiku.

  1. 09

    List projects

    List all DSS projects accessible to the API key

  2. 10

    Dataset schema

    Get the schema (columns, types) of a specific dataset

  3. 11

    Get model

    Get saved model metadata, algorithm, and performance metrics

Capability set04 / 04

12-14

3 capabilities in this set.

Part of 14 available through Dataiku.

  1. 12

    List models

    List deployed/saved ML models in a project

  2. 13

    Run scenario

    Trigger a scenario execution (build pipeline, retrain model)

  3. 14

    List plugins

    List installed DSS plugins

Observed, not estimated

902ms average. Fast in production.

Dataiku is checked daily against the live service.

Daily averagePeak 1245ms
Aug 20Today
Fastest day
736ms
Slowest day
1245ms
14-day trend
Slowing+21%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 14 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Dataiku, so you can see the experience inside your AI.

It does not authenticate your account with Dataiku. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Dataiku Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_omB2MsN4Y1V0ZX0dD6ibopTA1n2aAb9aFGAtDQOY/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Dataiku capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "dataiku-dss-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_omB2MsN4Y1V0ZX0dD6ibopTA1n2aAb9aFGAtDQOY/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

FAQ

Questions Dataiku owners ask.

  • 01

    Can my agent trigger a Dataiku automation scenario?

    Yes. Use the 'run_scenario' capability. Provide the project key and the scenario ID. The agent will command the backend to orchestrate the absolute workflow rules, triggering a new execution run for your pipeline or model retraining.

  • 02

    How do I check the schema of a specific dataset via chat?

    Provide the project key and dataset name to the 'dataset_schema' capability. Your agent will validate the API arrays structurally and return the dataset column names and types natively, helping you understand your data boundaries.

  • 03

    Can I monitor the performance of saved ML models?

    Absolutely. Use the 'get_model' capability. Your agent retrieves the metadata and performance metrics defining specific trained schema layers, allowing you to audit model quality and drift without opening the DSS UI.

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