Can my agent trigger a Dataiku automation scenario?

Yes. Use the 'run_scenario' tool. 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' tool. 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' 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.

Dataiku DSS MCP Server for Pydantic AI 14 tools — connect in under 2 minutes

Name: Dataiku DSS
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 14 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Dataiku DSS through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Dataiku DSS "
            "(14 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Dataiku DSS?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Dataiku DSS MCP Server

Connect your Dataiku DSS instance to any AI agent and take full control of your enterprise AI and collaborative data science workflows through natural conversation.

Pydantic AI validates every Dataiku DSS tool response against typed schemas, catching data inconsistencies at build time. Connect 14 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Project & Dataset Exploration — List all accessible DSS projects and retrieve structural extraction of dataset column schemas and types
Pipeline Orchestration — Monitor build tasks and training runs by listing pipeline jobs and analyzing execution states and timing
Transformation Auditing — Retrieve explicit configuration structures parsing precise Dataiku recipes (Python, SQL, Visual) to verify data logic
Automation & Scenarios — List automation scenarios and trigger execution commands to rebuild pipelines or retrain models securely
Model Monitoring — Identify saved ML models and retrieve detailed performance metrics defining specific trained schema layers
Admin Oversight — Enumerate installed plugins and data connections (SQL, Cloud Storage, APIs) to verify organizational constraints

The Dataiku DSS MCP Server exposes 14 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Dataiku DSS to Pydantic AI via MCP

Follow these steps to integrate the Dataiku DSS MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 14 tools from Dataiku DSS with type-safe schemas

Why Use Pydantic AI with the Dataiku DSS MCP Server

Pydantic AI provides unique advantages when paired with Dataiku DSS through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Dataiku DSS integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Dataiku DSS connection logic from agent behavior for testable, maintainable code

Dataiku DSS + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Dataiku DSS MCP Server delivers measurable value.

Type-safe data pipelines: query Dataiku DSS with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Dataiku DSS tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Dataiku DSS and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Dataiku DSS responses and write comprehensive agent tests

Dataiku DSS MCP Tools for Pydantic AI (14)

These 14 tools become available when you connect Dataiku DSS to Pydantic AI via MCP:

dataset_schema

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

get_job

Get job state, timing, and outputs

get_model

Get saved model metadata, algorithm, and performance metrics

get_project

Get project metadata, settings, and tags

get_recipe

Get recipe configuration and settings

list_connections

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

list_datasets

List all datasets in a project

list_jobs

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

list_models

List deployed/saved ML models in a project

list_plugins

List installed DSS plugins

list_projects

List all DSS projects accessible to the API key

list_recipes

List all recipes (data transformations) in a project

list_scenarios

List automation scenarios in a project

run_scenario

Trigger a scenario execution (build pipeline, retrain model)

Example Prompts for Dataiku DSS in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Dataiku DSS immediately.

"List all projects in my Dataiku instance"

"What is the schema for dataset 'raw_logs' in project 'FRAUD'?"

"Run scenario 'REBUILD_PIPELINE' in project 'SALES'"

Troubleshooting Dataiku DSS MCP Server with Pydantic AI

Common issues when connecting Dataiku DSS to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Dataiku DSS + Pydantic AI FAQ

Common questions about integrating Dataiku DSS MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Dataiku DSS MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.