Integrating Dataiku with MongoDB Data via CData Connect AI

Dataiku is a collaborative data science and AI platform that enables teams to design, deploy, and manage machine learning and generative AI projects within a governed environment. It's Agent and GenAI framework allows users to build intelligent agents that can analyze, generate, and act on data through custom workflows and model orchestration.

By integrating Dataiku with CData Connect AI through the built-in MCP (Model Context Protocol) Server, these agents gain secure, real-time access to live MongoDB data. The integration bridges Dataiku's agent execution environment with CData's governed enterprise connectivity layer, allowing every query or instruction to run safely against authorized data sources without manual exports or staging.

This article demonstrates how to configure MongoDB connectivity in Connect AI, prepare a Python code environment in Dataiku with MCP support, and create an agent that queries and interacts with live MongoDB data directly from within Dataiku.

Getting Started

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Step 1: Configure MongoDB Connectivity for Dataiku

Connectivity to MongoDB from Dataiku is made possible through CData Connect AI's Remote MCP Server. To interact with MongoDB data from Dataiku, you start by creating and configuring a MongoDB connection in CData Connect AI.

1. Log into Connect AI, click Sources, then click Add Connection
👁 Adding a Connection

2. Select "MongoDB" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to MongoDB.

   Set the Server, Database, User, and Password connection properties to connect to MongoDB. To access MongoDB collections as tables you can use automatic schema discovery or write your own schema definitions. Schemas are defined in .rsd files, which have a simple format. You can also execute free-form queries that are not tied to the schema.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Open the Permissions tab and set user-based permissions
👁 Updating permissions

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Dataiku. It is best practice to create a separate PAT for each integration to maintain granular access control

1. Click the gear icon () at the top right of the Connect AI app to open Settings
2. On the Settings page, go to the Access Tokens section and click Create PAT
3. Give the PAT a descriptive name and click Create
👁 Creating a new PAT

4. Copy the token when displayed and store it securely. It will not be shown again

With the MongoDB connection configured and a PAT generated, Dataiku can now connect to MongoDB data through the Connect AI.

Step 2: Prepare Dataiku and the Code Environment

A dedicated python code environment in Dataiku provides the runtime support needed for MCP-based communication. To enable Dataiku Agents to connect to CData Connect AI, create a Python environment and install the MCP client dependencies required for agent-to-server interaction.

1. In Dataiku Cloud, open Code Envs
👁 Dataiku Cloud Code Envs

2. Click Add a code env to open the DSS settings window
👁 Open DSS settings for Code Envs

3. In DSS, click New Python env. Name it (for example, MCP_Package) and choose Python 3.10 (3.10 to 3.13 supported)
👁 Create Python env

4. Open Packages to install and add the following pip packages:
• httpx
• anyio
• langchain-mcp-adapters
👁 Add MCP client dependencies

5. Open Containerized execution and under Container runtime additions select Agent tool MCP servers support
👁 Enable Agent tool MCP servers support

6. Check Rebuild env and click Save and update to install packages
7. Back in Dataiku Cloud, open Overview and click Open instance
👁 Open the DSS instance

8. Click + New project and select Blank project. Name the project
👁 Create a blank project

Step 3: Create a Dataiku Agent and connect to the MCP server

The Dataiku Agent serves as the bridge between the Dataiku workspace and Connect AI. To enable this connection, create a custom code-based agent, assign it the configured Python environment, and embed your Connect AI credentials to allow the agent to query and interact with live MongoDB data.

1. Go to Agents & GenAI Models and click Create your first agent
👁 Agents and GenAI Models

2. Choose Code agent, name it, and for Agent version select Asynchronous agent without streaming
👁 Agent starter selection

3. From the tab above select Settings. In Code env selection set Default Python code env to the environment you created (for example, MCP_Package)
👁 Project settings Code env selection

4. Return to the Agent Design tab and paste the following code. Replace EMAIL, and PAT with your values

import os
import base64
from typing import Dict, Any, List
 
from dataiku.llm.python import BaseLLM
from langchain_mcp_adapters.client import MultiServerMCPClient
 
# ---------- Persistent MCP client (cached between calls) ----------
_MCP_CLIENT = None
 
def _get_mcp_client() -> MultiServerMCPClient:
 """Create (or reuse) a MultiServerMCPClient to CData Cloud MCP."""
 global _MCP_CLIENT
 if _MCP_CLIENT is not None:
 return _MCP_CLIENT
 
 # Set creds via env/project variables ideally
 EMAIL = os.getenv("CDATA_EMAIL", "YOUR_EMAIL") 
 PAT = os.getenv("CDATA_PAT", "YOUR_PAT") 
 BASE_URL = "https://mcp.cloud.cdata.com/mcp"
 
 if not EMAIL or PAT == "YOUR_PAT":
 raise ValueError("Set CDATA_EMAIL and CDATA_PAT as env variables or inline in the code.")
 
 token = base64.b64encode(f"{EMAIL}:{PAT}".encode()).decode()
 headers = {"Authorization": f"Basic {token}"}
 
 _MCP_CLIENT = MultiServerMCPClient(
 connections={
 "cdata": {
 "transport": "streamable_http",
 "url": BASE_URL,
 "headers": headers,
 }
 }
 )
 return _MCP_CLIENT
 
 
def _pick_tool(tools, names: List[str]):
 L = [n.lower() for n in names]
 return next((t for t in tools if t.name.lower() in L), None)
 
 
async def _route(prompt: str) -> str:
 """
 Simple intent router:
 - 'list connections' / 'list catalogs' -> getCatalogs
 - 'sql: ...' or 'query: ...' -> queryData
 - otherwise -> help text
 """
 client = _get_mcp_client()
 tools = await client.get_tools()
 
 p = prompt.strip()
 low = p.lower()
 
 # 1) List connections (catalogs)
 if "list connections" in low or "list catalogs" in low:
 t = _pick_tool(tools, ["getCatalogs", "listCatalogs"])
 if not t:
 return "No 'getCatalogs' tool found on the MCP server."
 res = await t.ainvoke({})
 return str(res)[:4000]
 
 # 2) Run SQL
 if low.startswith("sql:") or low.startswith("query:"):
 sql = p.split(":", 1)[1].strip()
 t = _pick_tool(tools, ["queryData", "sqlQuery", "runQuery", "query"])
 if not t:
 return "No query-capable tool (queryData/sqlQuery) found on the MCP server."
 try:
 res = await t.ainvoke({"query": sql})
 return str(res)[:4000]
 except Exception as e:
 return f"Query failed: {e}"
 
 # 3) Help
 return (
 "Connected to CData MCP

"
 "Say **'list connections'** to view available sources, or run a SQL like:
"
 " sql: SELECT * FROM [Salesforce1].[SYS].[Connections] LIMIT 5

"
 "Remember to use bracket quoting for catalog/schema/table names."
 )
 
 
class MyLLM(BaseLLM):
 async def aprocess(self, query: Dict[str, Any], settings: Dict[str, Any], trace: Any):
 # Extract last user message from the Quick Test payload
 prompt = ""
 try:
 prompt = (query.get("messages") or [])[-1].get("content", "")
 except Exception:
 prompt = ""
 
 try:
 reply = await _route(prompt)
 except Exception as e:
 reply = f"Error: {e}"
 
 # The template expects a dict with a 'text' key
 return {"text": reply}

Run a Quick Test

1. Open Quick Test on the right side panel
2. Paste the JSON code and click Run test

{
 "messages": [
 {
 "role": "user",
 "content": "list connections"
 }
 ],
 "context": {}
}

Chat with your Agent

Switch to the Chat tab and try prompting like, "List all connections". The chat output will show a list of connection catalogs.

👁 Chat: listing catalogs and running queries


Get CData Connect AI

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