Integrating LangChain with Power BI XMLA Data via CData Connect AI

Dibyendu Datta
Lead Technology Evangelist

Leverage the CData Connect AI Remote MCP Server to enable LangChain agents to securely access and act on Power BI XMLA data within automated workflows.

LangChain is a framework used by developers, data engineers, and AI practitioners for building AI-powered applications and workflows by combining reasoning models (LLMs), tools, APIs, and data connectors. By integrating LangChain with CData Connect AI through the built-in MCP Server, workflows can effortlessly access and interact with live Power BI XMLA data in real time.

CData Connect AI offers a secure, low-code environment to connect Power BI XMLA and other data sources, removing the need for complex ETL and enabling seamless automation across business applications with live data.

This article outlines how to configure Power BI XMLA connectivity in CData Connect AI, register the MCP server with LangChain, and build a workflow that queries Power BI XMLA data in real time.

Prerequisites

An account in CData Connect AI
Python version 3.10 or higher, to install the LangChain and LangGraph packages
Generate and save an OpenAI API key
Install Visual Studio Code in your system

Step 1: Configure Power BI XMLA Connectivity for LangChain

Before LangChain can access Power BI XMLA, a Power BI XMLA connection must be created in CData Connect AI. This connection is then exposed to LangChain through the remote MCP server.

Log in to Connect AI click Sources, and then click + Add Connection
From the available data sources, choose Power BI XMLA
Enter the necessary authentication properties to connect to Power BI XMLA
By default, use Entra ID (formerly Azure AD) to connect to Microsoft Power BI XMLA. Entra ID (formerly Azure AD) is Microsoft's multi-tenant, cloud-based directory and identity management service. It is user-based authentication that requires that you set AuthScheme to EntraID (formerly AzureAD).

For more information on other authentication schemes, refer to the Help documentation.
Click Save & Test
Once authenticated, open the Permissions tab in the Power BI XMLA connection and configure user-based permissions as required

Generate a Personal Access Token (PAT)

LangChain authenticates to Connect AI using an account email and a Personal Access Token (PAT). Creating separate PATs for each integration is recommended to maintain access control granularity.

In Connect AI, select the Gear icon in the top-right to open Settings
Under Access Tokens, select Create PAT
Provide a descriptive name for the token and select Create
Copy the token and store it securely. The PAT will only be visible during creation

With the Power BI XMLA connection configured and a PAT generated, LangChain is prepared to connect to Power BI XMLA data through the CData MCP server.

Note: You can also generate a PAT from LangChain in the Integrations section of Connect AI. Simply click Connect --> Create PAT to generate it.

Navigate to the LangChain integration tool and click on Connect.

Step 2: Connect to the MCP server in LangChain

To connect LangChain with CData Connect AI Remote MCP Server and use OpenAI (ChatGPT) for reasoning, you need to configure your MCP server endpoint and authentication values in a config.py file. These values allow LangChain to call the MCP server tools, while OpenAI handles the natural language reasoning.

Create a folder for LangChain MCP
Create two Python files within the folder: config.py and langchain.py
In config.py, create a class Config to define your MCP server authentication and URL. You need to provide your Base64-encoded CData Connect AI username and PAT (obtained in the prerequisites):
```
 
class Config: 

      MCP_BASE_URL = "https://mcp.cloud.cdata.com/mcp"   #MCP Server URL 
      MCP_AUTH = "base64encoded(EMAIL:PAT)"   #Base64 encoded Connect AI Email:PAT 
```
Note: You can create the base64 encoded version of MCP_AUTH using any Base64 encoding tool.

In langchain.py, set up your MCP server and MCP client to call the tools and prompts:

 
""" 
Integrates a LangChain ReAct agent with CData Connect AI MCP server. 
The script demonstrates fetching, filtering, and using tools with an LLM for agent-based reasoning. 
""" 

import asyncio 
from langchain_mcp_adapters.client import MultiServerMCPClient 
from langchain_openai import ChatOpenAI 
from langgraph.prebuilt import create_react_agent 
from config import Config 

async def main(): 

# Initialize MCP client with one or more server URLs
mcp_client = MultiServerMCPClient( 
  connections={ 
    "default": { # you can name this anything 
    "transport": "streamable_http", 
    "url": Config.MCP_BASE_URL, 
    "headers": {"Authorization": f"Basic {Config.MCP_AUTH}"}, 
    } 
  } 
) 

# Load remote MCP tools exposed by the server 
all_mcp_tools = await mcp_client.get_tools() 
print("Discovered MCP tools:", [tool.name for tool in all_mcp_tools]) 

# Create and run the ReAct style agent 
llm = ChatOpenAI( 
  model="gpt-4o",  
  temperature=0.2, 
  api_key="YOUR_OPEN_API_KEY" #Use your OpenAI API Key here, this can be found here: https://platform.openai.com/ 
) 

agent = create_react_agent(llm, all_mcp_tools) 

user_prompt = "How many tables are available in PowerBIXMLA1?" #Change prompts as per need 
print(f"
User prompt: {user_prompt}") 

# Send a prompt asking the agent to use the MCP tools 
response = await agent.ainvoke( 
  { "messages": [{ "role": "user", "content": (user_prompt),}]} 
) 

# Print out the agent's final response 
final_msg = response["messages"][-1].content 
print("Agent final response:", final_msg) 

if __name__ == "__main__": 

asyncio.run(main())

Step 3: Install the LangChain and LangGraph packages

Since this workflow uses LangChain together with CData Connect AI MCP and integrates OpenAI for reasoning, you need to install the required Python packages.

Run the following command in your project terminal:

 
pip install langchain-mcp-adapters langchain-openai langgraph

Step 4: Prompt Power BI XMLA using LangChain (via the MCP server)

When the installation finishes, run python langchain.py to execute the script
The script connects to the MCP server and discovers the CData Connect AI MCP tools available for querying your connected data
Supply a prompt (e.g., "How many tables are available in Power BI XMLA?")
Accordingly, the agent responds with the results

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