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

Step 1: Configure Outreach.io Connectivity for Gumloop

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

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

2. Select "Outreach.io" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Outreach.io.

   You must use OAuth to authenticate with Outreach. Set the InitiateOAuth connection property to "GETANDREFRESH". For more information, refer to the OAuth section in the Help documentation.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Outreach.io 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 Outreach.io connection configured and a PAT generated, Gumloop is prepared to connect to Outreach.io 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
👁 Configuring MCP server
👁 MCP server app

4. Provide the following details:
   • URL: https://mcp.cloud.cdata.com/mcp
   • Label: A descriptive name such as Outreach.io-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 Outreach.io data with Gumloop

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

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

3. Search for Ask AI and select it
👁 Select Ask AI

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

5. From the MCP Servers dropdown, choose the saved MCP credential
6. Add a Prompt and Choose an AI Model according to your requirements
👁 Add Prompt

7. After configuring the required details, Click Run to run the pipeline
👁 Example 1: Gumloop workflow execution
👁 Example 2: Gumloop workflow execution

With the workflow run completed, Gumloop demonstrates successful retrieval of Outreach.io 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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