How to Connect to Live PingOne 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 PingOne 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 PingOne data in real-time through natural language queries. This article outlines the process of connecting to PingOne using Connect AI Remote MCP and configuring an OpenAI-powered Python application to interact with your PingOne data through conversational AI.

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

Step 1: Configure PingOne Connectivity for OpenAI Applications

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

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

Select "PingOne" from the Add Connection panel

Enter the necessary authentication properties to connect to PingOne.
To connect to PingOne, configure these properties:
- Region: The region where the data for your PingOne organization is being hosted.
- AuthScheme: The type of authentication to use when connecting to PingOne.
- Either WorkerAppEnvironmentId (required when using the default PingOne domain) or AuthorizationServerURL, configured as described below.
Configuring WorkerAppEnvironmentId

WorkerAppEnvironmentId is the ID of the PingOne environment in which your Worker application resides. This parameter is used only when the environment is using the default PingOne domain (auth.pingone). It is configured after you have created the custom OAuth application you will use to authenticate to PingOne, as described in Creating a Custom OAuth Application in the Help documentation.

First, find the value for this property:
1. From the home page of your PingOne organization, move to the navigation sidebar and click Environments.
2. Find the environment in which you have created your custom OAuth/Worker application (usually Administrators), and click Manage Environment. The environment's home page displays.
3. In the environment's home page navigation sidebar, click Applications.
4. Find your OAuth or Worker application details in the list.
5. Copy the value in the Environment ID field. It should look similar to:
```
WorkerAppEnvironmentId='11e96fc7-aa4d-4a60-8196-9acf91424eca'
```
Now set WorkerAppEnvironmentId to the value of the Environment ID field.

Configuring AuthorizationServerURL

AuthorizationServerURL is the base URL of the PingOne authorization server for the environment where your application is located. This property is only used when you have set up a custom domain for the environment, as described in the PingOne platform API documentation. See Custom Domains.

Authenticating to PingOne with OAuth

PingOne supports both OAuth and OAuthClient authentication. In addition to performing the configuration steps described above, there are two more steps to complete to support OAuth or OAuthCliet authentication:
- Create and configure a custom OAuth application, as described in Creating a Custom OAuth Application in the Help documentation.
- To ensure that the driver can access the entities in Data Model, confirm that you have configured the correct roles for the admin user/worker application you will be using, as described in Administrator Roles in the Help documentation.
- Set the appropriate properties for the authscheme and authflow of your choice, as described in the following subsections.
OAuth (Authorization Code grant)

Set AuthScheme to OAuth.

Desktop Applications

Get and Refresh the OAuth Access Token

After setting the following, you are ready to connect:
- InitiateOAuth: GETANDREFRESH. To avoid the need to repeat the OAuth exchange and manually setting the OAuthAccessToken each time you connect, use InitiateOAuth.
- OAuthClientId: The Client ID you obtained when you created your custom OAuth application.
- OAuthClientSecret: The Client Secret you obtained when you created your custom OAuth application.
- CallbackURL: The redirect URI you defined when you registered your custom OAuth application. For example: https://localhost:3333
When you connect, the driver opens PingOne's OAuth endpoint in your default browser. Log in and grant permissions to the application. The driver then completes the OAuth process:
1. The driver obtains an access token from PingOne and uses it to request data.
2. The OAuth values are saved in the location specified in OAuthSettingsLocation, to be persisted across connections.
The driver refreshes the access token automatically when it expires.

For other OAuth methods, including Web Applications, Headless Machines, or Client Credentials Grant, refer to the Help documentation.
Click Save & Test
Navigate to the Permissions tab in the Add PingOne 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 PingOne 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 PingOne 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 PingOne data
        agent = MCPAgent(
            name="data_assistant",
            model=model,
            mcp_servers=[mcp_server],
            instructions="""You are a data query assistant with access to PingOne data through CData Connect AI.

            You can help users explore and query their PingOne 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 PingOne 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 PingOne data through natural language queries. Your OpenAI assistant now has access to your PingOne data through the CData Connect AI MCP Server.

Step 3: Build Intelligent Applications with Live PingOne 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 PingOne 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 PingOne 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 PingOne 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 hundreds of SaaS, Big Data, and NoSQL sources directly from your OpenAI applications, try CData Connect AI today!

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

Step 1: Configure PingOne Connectivity for OpenAI Applications

Configuring WorkerAppEnvironmentId

Configuring AuthorizationServerURL

Authenticating to PingOne with OAuth

OAuth (Authorization Code grant)

Desktop Applications