Use Azure AI Foundry to Talk to Your PingOne Data via CData Connect AI

Azure AI Foundry is Microsoft's comprehensive platform for building, deploying, and managing AI applications and agents. It provides a unified environment for creating intelligent agents that can automate tasks, answer questions, and assist with various business processes. When combined with CData Connect AI Remote MCP, you can leverage Azure AI Foundry to interact with your PingOne data in real-time. This article outlines the process of connecting to PingOne using Connect AI Remote MCP and creating an agent in Azure AI Foundry to interact with your PingOne data.

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 Azure AI Foundry and PingOne. This allows you to ask questions and take actions on your PingOne data using Azure AI Foundry agents, all without the need for data replication to a natively supported database. 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 build an agent in Azure AI Foundry to conversationally explore (or Vibe Query) your data. The connectivity principles apply to any Azure AI Foundry agent. With Connect AI you can build AI agents with access to live PingOne data, plus hundreds of other sources.

Step 1: Create an Azure AI Foundry Resource

Before connecting to PingOne data, you'll need to create an Azure AI Foundry resource in your Azure portal.

1. Log into the Azure Portal.
2. Click Create a resource and search for Microsoft Foundry.
3. Click Create to begin the resource creation wizard.
4. In the Basics tab:
   • Select or create a Resource group
   • Enter a Name for your Foundry resource
   • Enter a Project name
   • Click Next
5. Configure the Storage, Network, Identity, Encryption, and Tags tabs according to your organization's requirements, clicking Next after each section.
6. On the Review + submit tab, review your settings and click Create.
7. Once the resource is created, click Go to resource.
8. Click Go to Foundry portal to access the Azure AI Foundry portal.

Step 2: Configure PingOne Connectivity for Azure AI Foundry

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

1. Log into Connect AI, click Connections and click Add Connection
2. Select "PingOne" from the Add Connection panel
3. 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
4. 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 Azure AI Foundry. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. 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 Azure AI Foundry.

Step 3: Create an AI Agent in Azure AI Foundry

Follow these steps to create an AI agent and connect it to CData Connect AI:

1. In the Azure AI Foundry portal, click New Foundry to create a new project.

2. Click Start building and then select Create agent.

3. Enter a Name for your agent.

   

4. In the Setup section:

   • Choose your preferred AI model
   • Configure Instructions for how the agent should behave
   

Step 4: Add the CData Connect AI MCP Tool

Now you'll add the CData Connect AI MCP Server as a custom tool for your agent:

1. In the agent setup, navigate to the Tools section and click Add.

2. Select Custom from the tool options.

3. Choose Model Context Protocol and click Create.

   

4. Enter a Name for the MCP tool (such as "CData Connect AI MCP Server").

5. In the Remote MCP Server endpoint field, enter: https://mcp.cloud.cdata.com/mcp/

6. For Authentication, select Key-based.

7. Configure the credential using:

   • Header name: Authorization
   • Value: Basic EMAIL:PAT, replacing EMAIL with your Connect AI email address and PAT with the personal access token you created earlier
   For example: Basic [email protected]:Uu90pt5vEO...

8. Click Connect to establish the connection to CData Connect AI.

   

Optional: Provide Agent Context

You can enhance your agent's understanding by providing specific instructions about using the MCP Server tools. In the agent's Instructions section, you can add guidance such as:

You are an expert at using the MCP Client tool connected to the CData Connect AI MCP Server. Always search thoroughly and use the most relevant MCP Client tool for each query. Below are the available tools and a description of each:

queryData: Execute SQL queries against connected data sources and retrieve results. When you use the queryData tool, ensure you use the following format for the table name: catalog.schema.tableName
getCatalogs: Retrieve a list of available connections from CData Connect AI. The connection names should be used as catalog names in other tools and in any queries to CData Connect AI. Use the `getSchemas` tool to get a list of available schemas for a specific catalog.
getSchemas: Retrieve a list of available database schemas from CData Connect AI for a specific catalog. Use the `getTables` tool to get a list of available tables for a specific catalog and schema.
getTables: Retrieve a list of available database tables from CData Connect AI for a specific catalog and schema. Use the `getColumns` tool to get a list of available columns for a specific table.
getColumns: Retrieve a list of available database columns from CData Connect AI for a specific catalog, schema, and table.
getProcedures: Retrieve a list of stored procedures from CData Connect AI for a specific catalog and schema
getProcedureParameters: Retrieve a list of stored procedure parameters from CData Connect AI for a specific catalog, schema, and procedure.
executeProcedure: Execute stored procedures with parameters against connected data sources

Step 5: Chat with Your PingOne Data

With your agent configured and connected to CData Connect AI, you can now interact with your PingOne data using natural language:

1. In the Azure AI Foundry portal, navigate to the Chat with data section of your agent.

2. Start asking questions about your PingOne data. For example:

   • "Show me all customers from the last 30 days"
   • "What are my top performing products?"
   • "Analyze sales trends for Q4"
   • "List all active projects with their current status"
   

3. The agent will use the CData Connect AI MCP Server to query your PingOne data in real-time and provide responses based on live data.

Step 6: Publish Your Agent

Once you're satisfied with your agent's configuration and testing, click Publish to make your agent available for use in your organization.

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