Integrating Gumloop with Kafka Data via CData Connect AI

Yazhini G
Yazhini G
Technical Marketing Engineer
Leverage the CData Connect AI Remote MCP Server to enable Gumloop to securely access and act on Kafka data within automated workflows.

Gumloop is a visual automation platform designed to create AI-powered workflows by combining triggers, AI nodes, APIs, and data connectors. By integrating Gumloop with CData Connect AI through the built-in MCP (Model Context Protocol) Server, workflows can seamlessly access and interact with live Kafka data.

The platform provides a low-code environment, making it easier to orchestrate complex processes without heavy development effort. Its flexibility allows integration across multiple business applications, enabling end-to-end automation with live data.

This article outlines the steps required to configure Kafka connectivity in Connect AI, register the MCP server in Gumloop, and build a workflow that queries Kafka data.

Step 1: Configure Kafka Connectivity for Gumloop

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

  1. Log into Connect AI, click Sources, and then click Add Connection
    2. Adding a Connection
  2. Select "Kafka" from the Add Connection panel
    3. Selecting a data source
  3. Enter the necessary authentication properties to connect to Kafka.

    Set BootstrapServers and the Topic properties to specify the address of your Apache Kafka server, as well as the topic you would like to interact with.

    Authorization Mechanisms

    • SASL Plain: The User and Password properties should be specified. AuthScheme should be set to 'Plain'.
    • SASL SSL: The User and Password properties should be specified. AuthScheme should be set to 'Scram'. UseSSL should be set to true.
    • SSL: The SSLCert and SSLCertPassword properties should be specified. UseSSL should be set to true.
    • Kerberos: The User and Password properties should be specified. AuthScheme should be set to 'Kerberos'.

    You may be required to trust the server certificate. In such cases, specify the TrustStorePath and the TrustStorePassword if necessary.

    Configuring a connection (Salesforce is shown)
  4. Click Save & Test
  5. Navigate to the Permissions tab in the Add Kafka Connection page and update the User-based permissions. Updating permissions

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Gumloop. 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. Creating a new PAT
  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 Kafka connection configured and a PAT generated, Gumloop is prepared to connect to Kafka data through the CData MCP server.

Step 2: Connect to the MCP server in Gumloop

The MCP server endpoint and authentication values from Connect AI must be added to Gumloop credentials.

  1. Sign in to Gumloop and create an account
  2. Visit the Gumloop Credentials page to configure MCP server
  3. Click on Add Credentials and search and select MCP Server
    4. Configuring MCP server MCP server app
  4. Provide the following details:
    • URL: https://mcp.cloud.cdata.com/mcp
    • Label: A descriptive name such as Kafka-mcp-server
    • Access Token / API Key: leave blank
    • Additional Header: Authorization: Basic YOUR EMAIL:YOUR PAT
      • Configuring to CData MCP server
    • Save the credentials
      • Saved MCP Credentials

The MCP server is now available to build workflows in Gumloop.

Step 3: Build a workflow and explore live Kafka data with Gumloop

  1. Visit Gumloop Personal workspace and click on the Create Flow
    2. Create Gumloop workflow
  2. Select the icon or press Ctrl + B to add a node or a subflow
    3. Add a node
  3. Search for Ask AI and select it
    4. Select Ask AI
  4. Click Show More Options and enable the Connect MCP Server? option
    5. Enable
  5. From the MCP Servers dropdown, choose the saved MCP credential
  6. Add a Prompt and Choose an AI Model according to your requirements
    7. Add Prompt
  7. After configuring the required details, Click Run to run the pipeline
    8. Example 1: Gumloop workflow execution Example 2: Gumloop workflow execution

With the workflow run completed, Gumloop demonstrates successful retrieval of Kafka 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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