Dataiku DSS MCP Server for LlamaIndex 14 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Dataiku DSS as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Dataiku DSS. "
"You have 14 tools available."
),
)
response = await agent.run(
"What tools are available in Dataiku DSS?"
)
print(response)
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.
LlamaIndex agents combine Dataiku DSS tool responses with indexed documents for comprehensive, grounded answers. Connect 14 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Dataiku DSS MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Dataiku DSS MCP Server
LlamaIndex provides unique advantages when paired with Dataiku DSS through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Dataiku DSS tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Dataiku DSS tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Dataiku DSS, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Dataiku DSS tools were called, what data was returned, and how it influenced the final answer
Dataiku DSS + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Dataiku DSS MCP Server delivers measurable value.
Hybrid search: combine Dataiku DSS real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Dataiku DSS to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Dataiku DSS for fresh data
Analytical workflows: chain Dataiku DSS queries with LlamaIndex's data connectors to build multi-source analytical reports
Dataiku DSS MCP Tools for LlamaIndex (14)
These 14 tools become available when you connect Dataiku DSS to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Dataiku DSS to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDataiku DSS + LlamaIndex FAQ
Common questions about integrating Dataiku DSS MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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.
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.
Connect Dataiku DSS to LlamaIndex
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.