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 LangChain 14 tools — connect in under 2 minutes

Name: Dataiku DSS
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 14 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Dataiku DSS through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "dataiku-dss": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Dataiku DSS, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

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Ed25519Audit chain

<40msKill switch

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.

LangChain's ecosystem of 500+ components combines seamlessly with Dataiku DSS through native MCP adapters. Connect 14 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

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

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 14 tools from Dataiku DSS via MCP

Why Use LangChain with the Dataiku DSS MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Dataiku DSS MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Dataiku DSS queries for multi-turn workflows

Dataiku DSS + LangChain Use Cases

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

RAG with live data: combine Dataiku DSS tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Dataiku DSS, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Dataiku DSS tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Dataiku DSS tool call, measure latency, and optimize your agent's performance

Dataiku DSS MCP Tools for LangChain (14)

These 14 tools become available when you connect Dataiku DSS to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Dataiku DSS + LangChain FAQ

Common questions about integrating Dataiku DSS MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.