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

Step 1: Configure your API Connectivity for Gumloop

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

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

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

3. Enter the necessary authentication properties to connect to your API.

   To connect to your API, configure the following properties on the Global Settings page:

   • In Authentication, select the Type and fill in the required properties
   • In Headers, add the required HTTP headers for your API
   • In Pagination, select the Type and fill in the required properties

   After the configuring the global settings, navigate to the Tables to add tables. For each table you wish to add:

   1. Click "+ Add"
   2. Set the Name for the table
   3. Set Request URL to the API endpoint you wish to work with 👁 Setting the Request URL (Harvest is shown)
      
   4. (Optional) In Parameters, add the required URL Parameters for your API endpoint
   5. (Optional) In Headers, add the required HTTP headers for the API endpoint
   6. In Table Data click " Configure"
   7. Review the response from the API and click "Next" 👁 Reviewing the API response (Harvest is shown)
      
   8. Select which element to use as the Repeated Elements and which elements to use as Columns and click "Next" 👁 Configuring the schema based on the API response(Harvest is shown)
      
   9. Preview the tabular model of the API response and click "Confirm" 👁 Previewing the tabular model of the API response (Harvest is shown)
      
4. Click Save & Test
5. Navigate to the Permissions tab in the Add your API 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 API connection configured and a PAT generated, Gumloop is prepared to connect to API 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 API-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 API 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 API 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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