Dataiku DSS MCP Server for AutoGen 14 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Dataiku DSS as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="dataiku_dss_agent",
tools=tools,
system_message=(
"You help users with Dataiku DSS. "
"14 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
asyncio.run(main())
* 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.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Dataiku DSS tools. Connect 14 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
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 AutoGen 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 AutoGen via MCP
Follow these steps to integrate the Dataiku DSS MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 14 tools from Dataiku DSS automatically
Why Use AutoGen with the Dataiku DSS MCP Server
AutoGen provides unique advantages when paired with Dataiku DSS through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Dataiku DSS tools to solve complex tasks
Role-based architecture lets you assign Dataiku DSS tool access to specific agents. a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Dataiku DSS tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Dataiku DSS tool responses in an isolated environment
Dataiku DSS + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Dataiku DSS MCP Server delivers measurable value.
Collaborative analysis: one agent queries Dataiku DSS while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Dataiku DSS, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Dataiku DSS data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Dataiku DSS responses in a sandboxed execution environment
Dataiku DSS MCP Tools for AutoGen (14)
These 14 tools become available when you connect Dataiku DSS to AutoGen 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 AutoGen
Ready-to-use prompts you can give your AutoGen 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 AutoGen
Common issues when connecting Dataiku DSS to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Dataiku DSS + AutoGen FAQ
Common questions about integrating Dataiku DSS MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
Connect Dataiku DSS with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
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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.
Connect Dataiku DSS to AutoGen
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.