Integrate Dataiku DSS with Claude, Cursor, Chatbots & AI Agents MCP Server
GDPR Free for SubscribersCompatible with every major AI agent and IDE
GeminiDataset schema on Dataiku DSS
Get the schema (columns, types) of a specific dataset
Get job on Dataiku DSS
Get job state, timing, and outputs
Get model on Dataiku DSS
Get saved model metadata, algorithm, and performance metrics
Get project on Dataiku DSS
Get project metadata, settings, and tags
Get recipe on Dataiku DSS
Get recipe configuration and settings
List connections on Dataiku DSS
List all DSS data connections (databases, cloud storage, APIs)
List datasets on Dataiku DSS
List all datasets in a project
List jobs on Dataiku DSS
List pipeline jobs in a project (build tasks, training runs)
List models on Dataiku DSS
List deployed/saved ML models in a project
List plugins on Dataiku DSS
List installed DSS plugins
List projects on Dataiku DSS
List all DSS projects accessible to the API key
List recipes on Dataiku DSS
List all recipes (data transformations) in a project
List scenarios on Dataiku DSS
List automation scenarios in a project
Run scenario on Dataiku DSS
Trigger a scenario execution (build pipeline, retrain model)
Security & Code Integrity Audit
Every tool in the Dataiku DSS MCP Server is continuously audited by the Vinkius Security Engine. We guarantee zero-trust payload isolation, strict data boundaries, and deterministic execution for enterprise-grade AI agents.
How Vinkius protects your data
Is there a risk of the AI "going crazy" and deleting important company data?
No. With Vinkius, the AI operates on "rails". It can only make the exact moves you authorized in the tool's settings. It cannot invent routes, access other networks in your company, or decide to delete random files. If the action isn't in the approved catalog, the attempt is blocked instantly.
Can I monitor the performance of saved ML models?
Absolutely. Use the 'get_model' tool. 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.
What happens if the underlying API rate limits my agent?
Our edge infrastructure automatically handles backoffs, queueing, and throttling. If an AI agent sends too many erratic requests, Vinkius manages the rate limits gracefully, ensuring your backend doesn't crash.
Does the AI train on my tools or API data?
No. Vinkius enforces a strict Zero-Retention policy. Your data simply passes through our secure servers to complete the requested action and is instantly forgotten. Nothing you do here is ever stored, logged, or used to train any artificial intelligence.
Supported Use Cases for Dataiku DSS
Integrate Dataiku DSS to provide your custom AI agents with direct read and write access to the capabilities listed below.
Cursor Copilot for data science
Integrate the Dataiku DSS server to handle data science requests natively. It provides the schemas required for ChatGPT and Cursor to manage ai frontier data.
Secure ml ops Access for Agents
Use the Dataiku DSS server to execute ml ops operations from your AI agent. The protocol manages state and authentication for continuous ai frontier workflows.
Dataiku DSS. Runs on everything.
From IDE to framework. Every connection governed by Vinkius.
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
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