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Vinkius

Dataiku DSS Connector for AI agents.

14 live capabilities

Manage your enterprise data science workflows and MLOps pipelines from a single chat.

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AI Agent

Why people use Dataiku DSS

Dataiku DSS for MLOps Pipeline Management

With this Connector, you can just ask your agent for the status of a specific job. It pulls the timing and state data immediately. You get a clear answer in your chat window, letting you stay focused on the high-level architecture instead of manual navigation.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a natural language interface for your entire Dataiku environment.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Quick schema verification

    A data scientist needs to know if the 'raw_logs' table has a timestamp column.

  2. Real-world use case 02

    Production job debugging

    An MLOps engineer sees a failed job and needs to know why.

  3. Real-world use case 03

    Inventory auditing

    A manager wants to see all active projects.

Complete set · 14capabilities

The complete Dataiku DSS capability set.

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

Capability set01 / 04

01—04

4 capabilities in this set.

Part of 14 available through Dataiku DSS.

  1. 01 Capability

    Get recipe

    Get the specific configuration and settings for a recipe. This is perfect for auditing logic.

  2. 02 Capability

    List jobs

    View all pipeline jobs, including build tasks and training runs. It helps you track active work.

  3. 03 Capability

    Get job

    Get the state, timing, and output of a specific job. Use this to troubleshoot failed runs.

  4. 04 Capability

    List connections

    List all data connections like databases or cloud storage. It helps you audit your data access points.

Capability set02 / 04

05—08

4 capabilities in this set.

Part of 14 available through Dataiku DSS.

  1. 05 Capability

    List projects

    See all projects available to your API key. It helps you quickly find the right workspace.

  2. 06 Capability

    Get project

    Pull metadata, settings, and tags for a specific project. Use this to understand project context.

  3. 07 Capability

    List datasets

    Get a list of all datasets within a project. This is the first step for data exploration.

  4. 08 Capability

    Dataset schema

    Retrieve columns and types for a dataset. It saves you from manually checking table headers.

Capability set03 / 04

09—11

3 capabilities in this set.

Part of 14 available through Dataiku DSS.

  1. 09 Capability

    List recipes

    See all data transformation recipes in a project. Use this to browse your available logic.

  2. 10 Capability

    List scenarios

    List all automation scenarios in a project. It shows you what can be triggered automatically.

  3. 11 Capability

    Run scenario

    Trigger a scenario execution to rebuild pipelines or retrain models. This automates your manual tasks.

Capability set04 / 04

12—14

3 capabilities in this set.

Part of 14 available through Dataiku DSS.

  1. 12 Capability

    List models

    See all deployed or saved ML models in a project. This is great for model inventory.

  2. 13 Capability

    Get model

    Get metadata, the algorithm used, and performance metrics for a model. Use it to evaluate results.

  3. 14 Capability

    List plugins

    See all installed DSS plugins. Use this to check your environment's capabilities.

Set up in minutes

One URL. Then ask Dataiku DSS to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Dataiku DSS from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_omB2MsN4Y1V0ZX0dD6ibopTA1n2aAb9aFGAtDQOY/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Dataiku DSS, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Dataiku DSS for the conversation.

Where the request belongs

Work Dataiku can move forward.

Built around the request

Data scientists and MLOps engineers who are tired of manual dashboard hopping and want to manage their production pipelines and models through a chat interface.

01

Data Scientist

Checks dataset schemas and model metrics during research without switching apps.

02

Data Engineer

Monitors pipeline jobs and audits recipe configurations to debug production issues.

03

MLOps Engineer

Triggers automation scenarios and monitors deployed models in real-time.

04

Analytics Manager

Audits project metadata and data connections across the company.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
DataRobot logo
01 6 capabilities

DataRobot

Manage AutoML via DataRobot. monitor projects and models, track deployments, and audit ML datasets directly from any AI agent.

View Connector
H2O.ai logo
02 6 capabilities

H2O.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.

View Connector
MindsDB (AI Database & Predictors) logo
03 6 capabilities

MindsDB (AI Database & Predictors)

Manage AI-powered data via MindsDB. execute SQL predictions, audit ML models, and connect data sources.

View Connector
Dagster logo
04 6 capabilities

Dagster

Orchestrate data pipelines via Dagster. monitor jobs, track runs, manage software-defined assets, and audit schedules directly from any AI agent.

View Connector
data.world logo
05 10 capabilities

data.world

Equip your AI agent to discover and manage data assets, projects, and queries directly via the data.world API.

View Connector
Neptune.ai (ML Experiment Tracking) logo
06 6 capabilities

Neptune.ai (ML Experiment Tracking)

Manage ML experiments via Neptune.ai. track training runs, monitor metrics, and audit model versions.

View Connector

Bring your own AI

Change the model, client or framework. Keep Dataiku connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
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  • Augment Code
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  • Tabnine
  • Pieces
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  • JetBrains
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  • LM Studio
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  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about Dataiku.

The practical details behind the request, access and result.

Can I use the Dataiku DSS MCP to see all my projects?

Yes, you can ask your agent to list every project your API key has access to. This helps you quickly navigate your workspace without manual searching.

How does the Dataiku DSS MCP help with MLOps?

It lets you monitor model performance metrics and trigger automation scenarios like retraining. You can manage the lifecycle of your models directly from your AI client.

Can I check the logic of a Dataiku recipe using this?

Yes, you can retrieve the configuration for Python, SQL, or Visual recipes. This is great for auditing data logic or debugging production pipelines.

Does the Dataiku DSS MCP support different data connections?

You can use it to list all your data connections, including databases, cloud storage, and APIs. It provides a clear overview of your organizational data access.

Can I run automation scenarios with the Dataiku DSS MCP?

You can trigger specific scenarios to rebuild pipelines or retrain models. It turns your manual maintenance tasks into simple natural language commands.

Is the Dataiku DSS MCP good for checking dataset schemas?

It's perfect for that. You can get the column names and types for any dataset in a project instantly, which speeds up your data exploration phase.

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.

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.

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.

One connection away

Give your agent a direct line to Dataiku.

Connect Dataiku once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available