Integrating Gumloop with Confluence Data via CData Connect AI

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 Confluence 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 Confluence connectivity in Connect AI, register the MCP server in Gumloop, and build a workflow that queries Confluence data.

Step 1: Configure Confluence Connectivity for Gumloop

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

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

2. Select "Confluence" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Confluence.

   Obtaining an API Token

   An API token is necessary for account authentication. To generate one, login to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

   Connect Using a Confluence Cloud Account

   To connect to a Cloud account, provide the following (Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.):

   • User: The user which will be used to authenticate with the Confluence server.
   • APIToken: The API Token associated with the currently authenticated user.
   • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.

   Connect Using a Confluence Server Instance

   To connect to a Server instance, provide the following:

   • User: The user which will be used to authenticate with the Confluence instance.
   • Password: The password which will be used to authenticate with the Confluence server.
   • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Confluence 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 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.
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 Confluence connection configured and a PAT generated, Gumloop is prepared to connect to Confluence 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. Provide the following details:
   • URL: https://mcp.cloud.cdata.com/mcp
   • Label: A descriptive name such as Confluence-mcp-server
   • Access Token / API Key: leave blank
   • Additional Header: Authorization: Basic YOUR EMAIL:YOUR PAT
   
   • Save the credentials
   

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

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

1. Visit Gumloop Personal workspace and click on the Create Flow

2. Select the icon or press Ctrl + B to add a node or a subflow

3. Search for Ask AI and select it

4. Click Show More Options and enable the Connect MCP Server? option

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. After configuring the required details, Click Run to run the pipeline

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