Integrating LlamaIndex with Okta 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 Okta data as native tools without writing custom connectors.

CData Connect AI offers a secure, low-code environment to connect Okta 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 Okta connectivity in CData Connect AI, register the MCP server with LlamaIndex, and build a ReAct agent that queries Okta 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 Okta Connectivity for LlamaIndex

Before LlamaIndex can access Okta, a Okta 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 Okta
3. Enter the necessary authentication properties to connect to Okta

   To connect to Okta, set the Domain connection string property to your Okta domain.

   You will use OAuth to authenticate with Okta, so you need to create a custom OAuth application.

   Creating a Custom OAuth Application

   From your Okta account:

   1. Sign in to your Okta developer edition organization with your administrator account.
   2. In the Admin Console, go to Applications > Applications.
   3. Click Create App Integration.
   4. For the Sign-in method, select OIDC - OpenID Connect.
   5. For Application type, choose Web Application.
   6. Enter a name for your custom application.
   7. Set the Grant Type to Authorization Code. If you want the token to be automatically refreshed, also check Refresh Token.
   8. Set the callback URL:
      • For desktop applications and headless machines, use http://localhost:33333 or another port number of your choice. The URI you set here becomes the CallbackURL property.
      • For web applications, set the callback URL to a trusted redirect URL. This URL is the web location the user returns to with the token that verifies that your application has been granted access.
   9. In the Assignments section, either select Limit access to selected groups and add a group, or skip group assignment for now.
   10. Save the OAuth application.
   11. The application's Client Id and Client Secret are displayed on the application's General tab. Record these for future use. You will use the Client Id to set the OAuthClientId and the Client Secret to set the OAuthClientSecret.
   12. Check the Assignments tab to confirm that all users who must access the application are assigned to the application.
   13. On the Okta API Scopes tab, select the scopes you wish to grant to the OAuth application. These scopes determine the data that the app has permission to read, so a scope for a particular view must be granted for the driver to have permission to query that view. To confirm the scopes required for each view, see the view-specific pages in Data Model < Views in the Help documentation.
   
4. Click Save & Test
5. Once authenticated, open the Permissions tab in the Okta 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 Okta connection configured and a PAT generated, LlamaIndex is prepared to connect to Okta 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 Okta1?"  # 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 Okta 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 Okta?")
4. The agent reasons over the available tools, calls

   queryData

   against Okta, and responds with the result

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