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

Step 1: Configure Greenhouse Connectivity for Gumloop

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

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

2. Select "Greenhouse" from the Add Connection panel

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

   You need an API key to connect to Greenhouse. To create an API key, follow the steps below:

   1. Click the Configure icon in the navigation bar and locate Dev Center on the left.
   2. Select API Credential Management.
   3. Click Create New API Key.
   • Set "API Type" to Harvest.
   • Set "Partner" to custom.
   • Optionally, provide a description.
   4. Proceed to Manage permissions and select the appropriate permissions based on the resources you want to access through the driver.
   5. Copy the created key and set APIKey to that value.
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Greenhouse 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 Greenhouse connection configured and a PAT generated, Gumloop is prepared to connect to Greenhouse 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 Greenhouse-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 Greenhouse 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 Greenhouse 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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