Dataiku DSS MCP Server for CrewAI 14 tools — connect in under 2 minutes
Connect your CrewAI agents to Dataiku DSS through Vinkius, pass the Edge URL in the `mcps` parameter and every Dataiku DSS tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="Dataiku DSS Specialist",
goal="Help users interact with Dataiku DSS effectively",
backstory=(
"You are an expert at leveraging Dataiku DSS tools "
"for automation and data analysis."
),
# Your Vinkius token. get it at cloud.vinkius.com
mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)
task = Task(
description=(
"Explore all available tools in Dataiku DSS "
"and summarize their capabilities."
),
agent=agent,
expected_output=(
"A detailed summary of 14 available tools "
"and what they can do."
),
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
* 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.
When paired with CrewAI, Dataiku DSS becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Dataiku DSS tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
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 CrewAI 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 CrewAI via MCP
Follow these steps to integrate the Dataiku DSS MCP Server with CrewAI.
Install CrewAI
Run pip install crewai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Customize the agent
Adjust the role, goal, and backstory to fit your use case
Run the crew
Run python crew.py. CrewAI auto-discovers 14 tools from Dataiku DSS
Why Use CrewAI with the Dataiku DSS MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Dataiku DSS through the Model Context Protocol.
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Dataiku DSS + CrewAI Use Cases
Practical scenarios where CrewAI combined with the Dataiku DSS MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries Dataiku DSS for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff
Scheduled intelligence reports: set up a crew that periodically queries Dataiku DSS, analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain Dataiku DSS tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow
Compliance and audit automation: a compliance agent queries Dataiku DSS against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
Dataiku DSS MCP Tools for CrewAI (14)
These 14 tools become available when you connect Dataiku DSS to CrewAI 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 CrewAI
Ready-to-use prompts you can give your CrewAI 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 CrewAI
Common issues when connecting Dataiku DSS to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
Dataiku DSS + CrewAI FAQ
Common questions about integrating Dataiku DSS MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.Can different agents in the same crew use different MCP servers?
mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.What happens when an MCP tool call fails during a crew run?
Can CrewAI agents call multiple MCP tools in parallel?
process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.Can I run CrewAI crews on a schedule (cron)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect Dataiku DSS with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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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.
Connect Dataiku DSS to CrewAI
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.