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

Step 1: Configure PagerDuty Connectivity for Gumloop

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

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

2. Select "PagerDuty" from the Add Connection panel

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

   Start by setting the Profile connection property to the location of the PagerDuty Profile on disk (e.g. C:\profiles\PagerDuty.apip). Next, set the ProfileSettings connection property to the connection string for PagerDuty (see below).

   PagerDuty API Profile Settings

   Register an OAuth application via PagerDuty's Developer Mode to obtain a Client ID and Client Secret. The callback URL must match the redirect URI configured in your app settings.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add PagerDuty 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 PagerDuty connection configured and a PAT generated, Gumloop is prepared to connect to PagerDuty 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 PagerDuty-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 PagerDuty 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 PagerDuty 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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