How to Connect to Live Okta Data from OpenAI Python Applications (via CData Connect AI)

Jerod Johnson
Director, Technology Evangelism

Leverage the CData Connect AI Remote MCP Server to enable OpenAI-powered Python applications to securely read and take actions on your Okta data through natural language.

OpenAI's Python SDK provides powerful capabilities for building AI applications that can interact with various data sources. When combined with CData Connect AI Remote MCP, you can build intelligent chat applications that interact with your Okta data in real-time through natural language queries. This article outlines the process of connecting to Okta using Connect AI Remote MCP and configuring an OpenAI-powered Python application to interact with your Okta data through conversational AI.

CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to Okta data. The CData Connect AI Remote MCP Server enables secure communication between OpenAI applications and Okta. This allows your AI assistants to read from and take actions on your live Okta data. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported SQL operations, including filters and JOINs, directly to Okta. This leverages server-side processing to swiftly deliver the requested Okta data.

In this article, we show how to configure an OpenAI-powered Python application to conversationally explore (or Vibe Query) your data using natural language. With Connect AI you can build AI assistants with access to live Okta data, plus hundreds of other sources.

Step 1: Configure Okta Connectivity for OpenAI Applications

Connectivity to Okta from OpenAI applications is made possible through CData Connect AI Remote MCP. To interact with Okta data from your OpenAI assistant, we start by creating and configuring a Okta connection in CData Connect AI.

Log into Connect AI, click Sources, and then click Add Connection

Select "Okta" from the Add Connection panel

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.
Click Save & Test
Navigate to the Permissions tab in the Add Okta Connection page and update the User-based permissions.

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from your OpenAI application. It is best practice to create a separate PAT for each service to maintain granularity of access.

Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
On the Settings page, go to the Access Tokens section and click Create PAT.
Give the PAT a name and click Create.
The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, we are ready to connect to Okta data from your OpenAI application.

Step 2: Configure Your OpenAI Python Application for CData Connect AI

Follow these steps to configure your OpenAI Python application to connect to CData Connect AI. You can use our pre-built client as a starting point, available at https://github.com/CDataSoftware/openai-mcp-client, or follow the instructions below to create your own.

Ensure you have Python 3.8+ installed and install the required dependencies:
```
pip install openai python-dotenv httpx
```

Clone or download the OpenAI MCP client from GitHub:

git clone https://github.com/CDataSoftware/openai-mcp-client.git
cd openai-mcp-client

Set up your environment variables. Create a .env file in your project root with the following variables:
```
OPENAI_API_KEY=YOUR_OPENAI_API_KEY
MCP_SERVER_URL=https://mcp.cloud.cdata.com/mcp
MCP_USERNAME=YOUR_EMAIL
MCP_PASSWORD=YOUR_PAT
OPENAI_MODEL=gpt-4
    
```
Replace YOUR_OPENAI_API_KEY with your OpenAI API key, YOUR_EMAIL with your Connect AI email address, and YOUR_PAT with the Personal Access Token created in Step 1.

If creating your own application, here's the core implementation for connecting to CData Connect AI MCP Server:

import os
import asyncio
import base64
from dotenv import load_dotenv
from mcp_client import MCPServerStreamableHttp, MCPAgent

# Load environment variables
load_dotenv()

async def main():
    """Main chat loop for interacting with Okta data."""
    # Get configuration
    api_key = os.getenv('OPENAI_API_KEY')
    mcp_url = os.getenv('MCP_SERVER_URL', 'https://mcp.cloud.cdata.com/mcp')
    username = os.getenv('MCP_USERNAME', '')
    password = os.getenv('MCP_PASSWORD', '')
    model = os.getenv('OPENAI_MODEL', 'gpt-4')

    # Create auth header for MCP server
    headers = {}
    if username and password:
        auth = base64.b64encode(f"{username}:{password}".encode()).decode()
        headers = {"Authorization": f"Basic {auth}"}

    # Connect to CData MCP Server
    async with MCPServerStreamableHttp(
        name="CData MCP Server",
        params={
            "url": mcp_url,
            "headers": headers,
            "timeout": 30,
            "verify_ssl": True
        }
    ) as mcp_server:

        # Create AI agent with access to Okta data
        agent = MCPAgent(
            name="data_assistant",
            model=model,
            mcp_servers=[mcp_server],
            instructions="""You are a data query assistant with access to Okta data through CData Connect AI.

            You can help users explore and query their Okta data in real-time.
            Use the available MCP tools to:
            - List available databases and schemas
            - Explore table structures
            - Execute SQL queries
            - Provide insights about the data

            Always explain what you're doing and format results clearly.""",
            api_key=api_key
        )

        await agent.initialize()
        print(f"Connected! {len(agent._tools_cache)} tools available.")
        print("
Chat with your Okta data (type 'exit' to quit):
")

        # Interactive chat loop
        conversation = []
        while True:
            user_input = input("You: ")
            if user_input.lower() in ['exit', 'quit']:
                break

            conversation.append({"role": "user", "content": user_input})

            print("Assistant: ", end="", flush=True)
            response = await agent.run(conversation)
            print(response["content"])

            conversation.append({"role": "assistant", "content": response["content"]})

if __name__ == "__main__":
    asyncio.run(main())

Run your OpenAI application:
```
python client.py
```
Start interacting with your Okta data through natural language queries. Your OpenAI assistant now has access to your Okta data through the CData Connect AI MCP Server.

Step 3: Build Intelligent Applications with Live Okta Data Access

With your OpenAI Python application configured and connected to CData Connect AI, you can now build sophisticated AI assistants that interact with your Okta data using natural language. The MCP integration provides your applications with powerful data access capabilities through OpenAI's advanced language models.

Available MCP Tools for Your Assistant

Your OpenAI assistant has access to the following CData Connect AI MCP tools:

queryData: Execute SQL queries against connected data sources and retrieve results
getCatalogs: Retrieve a list of available connections from CData Connect AI
getSchemas: Retrieve database schemas for a specific catalog
getTables: Retrieve database tables for a specific catalog and schema
getColumns: Retrieve column metadata for a specific table
getProcedures: Retrieve stored procedures for a specific catalog and schema
getProcedureParameters: Retrieve parameter metadata for stored procedures
executeProcedure: Execute stored procedures with parameters

Example Use Cases

Here are some examples of what your OpenAI-powered applications can do with live Okta data access:

Conversational Analytics: Build chat interfaces that answer complex business questions using natural language
Automated Reporting: Generate dynamic reports and summaries based on real-time data queries
Data Discovery Assistant: Help users explore and understand their data structure without SQL knowledge
Intelligent Data Monitor: Create AI assistants that proactively identify trends and anomalies
Custom Query Builder: Enable users to create complex queries through conversational interactions

Interacting with Your Assistant

Once running, you can interact with your OpenAI assistant through natural language. Example queries include:

"Show me all available databases"
"What tables are in the sales database?"
"List the top 10 customers by revenue"
"Find all orders from the last month"
"Analyze the trend in sales over the past quarter"
"What's the structure of the customer table?"

Your OpenAI assistant will automatically translate these natural language queries into appropriate SQL queries and execute them against your Okta data through the CData Connect AI MCP Server, providing intelligent insights without requiring users to write complex SQL or understand the underlying data structure.

Advanced Features

The OpenAI MCP integration supports advanced capabilities:

Context Awareness: The assistant maintains conversation context for follow-up questions
Multi-turn Conversations: Build complex queries through iterative dialogue
Intelligent Error Handling: Get helpful suggestions when queries encounter issues
Data Insights: Leverage GPT's analytical capabilities to identify patterns and trends
Format Flexibility: Request results in various formats (tables, summaries, JSON, etc.)

Get CData Connect AI

To get live data access to 300+ SaaS, Big Data, and NoSQL sources directly from your OpenAI applications, try CData Connect AI today!

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