How to Connect Flowise AI Agents to Live Azure Data Lake Storage Data via CData Connect AI

Integrate Flowise AI with the CData Connect AI MCP Server to enable agents to securely query and act on live data without replication.

Flowise AI is an open-source, no-code tool for building AI workflows and custom agents visually. Its drag-and-drop interface allows you to integrate large language models (LLMs) with APIs, databases, and external systems effortlessly.

CData Connect AI enables real-time connectivity to over 350+ enterprise data sources. Through its Model Context Protocol (MCP) server, CData Connect AI bridges Flowise agents with live Azure Data Lake Storage securely and efficiently, no data replication required. By combining Flowise AI's intuitive agent builder with CData's MCP integration, users can create agents capable of fetching, analyzing, and acting upon live Azure Data Lake Storage data directly within Flowise AI workflows.

This guide shows you how to connect Flowise AI to CData Connect AI MCP, set up credentials, and enable your agents to query live Azure Data Lake Storage data in real time.

Step 1: Configure Azure Data Lake Storage Connectivity for Flowise

Connectivity to Azure Data Lake Storage from Flowise AI is made possible through CData Connect AI's Remote MCP Server. To interact with Azure Data Lake Storage data from Flowise AI, we start by creating and configuring a Azure Data Lake Storage connection in CData Connect AI.

  1. Log into Connect AI, click Sources, and then click Add Connection
  2. Select Azure Data Lake Storage from the Add Connection panel
  3. Enter the necessary authentication properties to connect to Azure Data Lake Storage.

    Authenticating to a Gen 1 DataLakeStore Account

    Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

    For this, an Active Directory web application is required. You can create one as follows:

    1. Sign in to your Azure Account through the .
    2. Select "Entra ID" (formerly Azure AD).
    3. Select "App registrations".
    4. Select "New application registration".
    5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
    6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
    7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

    To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

    • Schema: Set this to ADLSGen1.
    • Account: Set this to the name of the account.
    • OAuthClientId: Set this to the application Id of the app you created.
    • OAuthClientSecret: Set this to the key generated for the app you created.
    • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
    • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

    Authenticating to a Gen 2 DataLakeStore Account

    To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

    • Schema: Set this to ADLSGen2.
    • Account: Set this to the name of the account.
    • FileSystem: Set this to the file system which will be used for this account.
    • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
    • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
  4. Click Save & Test
  5. Navigate to the Permissions tab and update user-based permissions

Once the connection is established, Azure Data Lake Storage data is now accessible in CData Connect AI and ready to be used with MCP enabled tools.

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Flowise AI. It is best practice to create a separate PAT for each integration to maintain granular access control.

  1. Click the gear icon () at the top right of the Connect AI app to open Settings
  2. On the Settings page, go to the Access Tokens section and click Create PAT
  3. Give the PAT a descriptive name and click Create
  4. Copy the token when displayed and store it securely. It will not be shown again

With the Azure Data Lake Storage connection configured and a PAT generated, Flowise AI can now connect to Azure Data Lake Storage data through the CData MCP Server.

Step 2: Configure Connect AI credentials in Flowise AI

Log in to Flowise AI workspace to set up the integration.

Add OpenAI credentials

  1. Navigate to Credentials and choose Add Credential
  2. Select OpenAI API from the dropdown
  3. Provide a name (e.g., OpenAI_Key) and paste the API key

Add the PAT variable

  1. Navigate to Variables and Add Variable
  2. Set Variable Name (e.g., PAT), choose Static as type, and set the Value to Base64-encoded username:PAT
  3. Click Add to save the variable

Step 3: Build the agent in Flowise AI

  1. Go to Agent Flows, select Add New
  2. Click the "+" icon to add a new node and choose Agent and drag the agent to the workflow
  3. Connect the Start node to the Agent node

Configure agent settings

Double-click on the Agent node and fill in the details:

  • Model: select ChatOpenAI or preferred model (e.g., gpt-4o-mini)
  • Connect Credential: Select OpenAI API key credential which was created earlier
  • Streaming: Enabled

Add the custom MCP tool

  1. Under Tools, click Add Tool and choose Custom MCP
  2. Fill in the JSON parameters as shown below:
 
{
  "url": "https://mcp.cloud.cdata.com/mcp",
  "headers": {
    "Authorization": "Basic {{$vars.PAT}}"
  }
}

Click the refresh icon to load available MCP actions. Once actions are listed, now Flowise agent is successfully connected to CData Connect AI MCP.

Step 4: Test and query live Azure Data Lake Storage data in Flowise

  1. Open the Chat tab in Flowise
  2. Type a query such as "Show top 10 records from Azure Data Lake Storage data table"
  3. Observe that responses are fetched in real time via the CData Connect AI MCP connection

With the workflow run completed, Flowise demonstrates successful retrieval of Salesforce data through the CData Connect AI MCP server, with the MCP Client node providing the ability to ask questions, retrieve records, and perform actions on the data.

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