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

Anusha M B
Anusha M B
Technical Marketing Engineer
Leverage the CData Connect AI Remote MCP Server to enable Azure AI Foundry agents to securely answer questions and take actions on your SingleStore data for you.

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 SingleStore data in real-time. This article outlines the process of connecting to SingleStore using Connect AI Remote MCP and creating an agent in Azure AI Foundry to interact with your SingleStore data.

CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to SingleStore data. The CData Connect AI Remote MCP Server enables secure communication between Azure AI Foundry and SingleStore. This allows you to ask questions and take actions on your SingleStore 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 SingleStore. This leverages server-side processing to swiftly deliver the requested SingleStore 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 SingleStore data, plus hundreds of other sources.

Step 1: Create an Azure AI Foundry Resource

Before connecting to SingleStore 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 SingleStore Connectivity for Azure AI Foundry

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

  1. Log into Connect AI, click Connections and click Add Connection
  2. Select "SingleStore" from the Add Connection panel
  3. Enter the necessary authentication properties to connect to SingleStore.

    The following connection properties are required in order to connect to data.

    • Server: The host name or IP of the server hosting the SingleStore database.
    • Port: The port of the server hosting the SingleStore database.
    • Database (Optional): The default database to connect to when connecting to the SingleStore Server. If this is not set, tables from all databases will be returned.

    Connect Using Standard Authentication

    To authenticate using standard authentication, set the following:

    • User: The user which will be used to authenticate with the SingleStore server.
    • Password: The password which will be used to authenticate with the SingleStore server.

    Connect Using Integrated Security

    As an alternative to providing the standard username and password, you can set IntegratedSecurity to True to authenticate trusted users to the server via Windows Authentication.

    Connect Using SSL Authentication

    You can leverage SSL authentication to connect to SingleStore data via a secure session. Configure the following connection properties to connect to data:

    • SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
    • SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
    • SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
    • SSLClientCertType: The certificate type of the client store.
    • SSLServerCert: The certificate to be accepted from the server.

    Connect Using SSH Authentication

    Using SSH, you can securely login to a remote machine. To access SingleStore data via SSH, configure the following connection properties:

    • SSHClientCert: Set this to the name of the certificate store for the client certificate.
    • SSHClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
    • SSHClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
    • SSHClientCertType: The certificate type of the client store.
    • SSHPassword: The password that you use to authenticate with the SSH server.
    • SSHPort: The port used for SSH operations.
    • SSHServer: The SSH authentication server you are trying to authenticate against.
    • SSHServerFingerPrint: The SSH Server fingerprint used for verification of the host you are connecting to.
    • SSHUser: Set this to the username that you use to authenticate with the SSH server.
    Click Save & Test
  4. Navigate to the Permissions tab in the Add SingleStore 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 SingleStore 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 SingleStore Data

With your agent configured and connected to CData Connect AI, you can now interact with your SingleStore 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 SingleStore 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 SingleStore 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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