Integrating LlamaIndex with Azure Analysis Services Data via CData Connect AI

LlamaIndex is a data framework for building LLM applications — agents, RAG pipelines, and structured workflows that reason over external data. By integrating LlamaIndex with CData Connect AI through the built-in MCP Server, your agents can discover and query live Azure Analysis Services data as native tools without writing custom connectors.

CData Connect AI offers a secure, low-code environment to connect Azure Analysis Services 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 Azure Analysis Services connectivity in CData Connect AI, register the MCP server with LlamaIndex, and build a ReAct agent that queries Azure Analysis Services data in real time.

Prerequisites

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

Step 1: Configure Azure Analysis Services Connectivity for LlamaIndex

Before LlamaIndex can access Azure Analysis Services, a Azure Analysis Services connection must be created in CData Connect AI. This connection is then exposed to LlamaIndex through the remote MCP server.

1. Log in to Connect AI, click Sources, and then click + Add Connection
2. From the available data sources, choose Azure Analysis Services
3. Enter the necessary authentication properties to connect to Azure Analysis Services

   To connect to Azure Analysis Services, set the Url property to a valid server, for instance, asazure://southcentralus.asazure.windows.net/server, in addition to authenticating. Optionally, set Database to distinguish which Azure database on the server to connect to.

   Azure Analysis Services uses the OAuth authentication standard. OAuth requires the authenticating user to interact with Azure Analysis Services using the browser. You can connect without setting any connection properties for your user credentials. See the Help documentation for more information.

   
4. Click Save & Test
5. Once authenticated, open the Permissions tab in the Azure Analysis Services connection and configure user-based permissions as required

Generate a Personal Access Token (PAT)

LlamaIndex 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.

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

With the Azure Analysis Services connection configured and a PAT generated, LlamaIndex is prepared to connect to Azure Analysis Services data through the CData MCP server.

Step 2: Connect to the MCP server in LlamaIndex

To connect LlamaIndex with CData Connect AI Remote MCP Server and use OpenAI for reasoning, configure your MCP server endpoint and authentication in a

config.py

file. These values let LlamaIndex’s MCP tool spec call the MCP server tools, while OpenAI handles the natural language reasoning.

1. Create a folder for the LlamaIndex MCP project
2. Create two Python files within the folder:

   config.py

   and

   llamaindex_agent.py

3. In

   config.py

   , define your MCP server URL and your Base64-encoded CData Connect AI email 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.

4. In

   llamaindex_agent.py

   , wire up the MCP tool spec and a ReAct agent:

   """
   Integrates a LlamaIndex ReAct agent with the CData Connect AI MCP server.
   The script discovers MCP tools, wraps them as LlamaIndex tools, and runs an
   agent loop driven by OpenAI for reasoning.
   """
   
   import asyncio
   from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
   from llama_index.core.agent.workflow import ReActAgent
   from llama_index.llms.openai import OpenAI
   from config import Config
   
   async def main():
   
       # Initialize the MCP client pointed at Connect AI
       mcp_client = BasicMCPClient(
           Config.MCP_BASE_URL,
           headers={"Authorization": f"Basic {Config.MCP_AUTH}"},
       )
   
       # Discover tools the MCP server exposes (getCatalogs, queryData, etc.)
       tool_spec = McpToolSpec(client=mcp_client)
       tools = await tool_spec.to_tool_list_async()
       print("Discovered MCP tools:", [t.metadata.name for t in tools])
   
       # Configure the LLM that drives the ReAct loop
       llm = OpenAI(
           model="gpt-4o",
           temperature=0.2,
           api_key="YOUR_OPENAI_API_KEY",  # https://platform.openai.com/
       )
   
       # Build the agent with the MCP-backed tools
       agent = ReActAgent(tools=tools, llm=llm)
   
       user_prompt = "How many tables are available in AAS1?"  # Change as needed
       print(f"
   User prompt: {user_prompt}")
   
       response = await agent.run(user_prompt)
   
       print("Agent final response:", response)
   
   if __name__ == "__main__":
       asyncio.run(main())
   

Step 3: Install the LlamaIndex packages

Since this workflow uses LlamaIndex together with the CData Connect AI MCP server and OpenAI for reasoning, install the required Python packages.

Run the following command in your project terminal:

pip install llama-index llama-index-tools-mcp llama-index-llms-openai

Step 4: Prompt Azure Analysis Services using LlamaIndex (via the MCP server)

1. When the installation finishes, run

   python llamaindex_agent.py

   to execute the script
2. The script connects to the MCP server and discovers the CData Connect AI MCP tools available for querying your connected data
3. Supply a prompt (e.g., "How many tables are available in Azure Analysis Services?")
4. The agent reasons over the available tools, calls

   queryData

   against Azure Analysis Services, and responds with the result

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