Integrating Gumloop with Printify 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 Printify 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 Printify connectivity in Connect AI, register the MCP server in Gumloop, and build a workflow that queries Printify data.
Step 1: Configure Printify Connectivity for Gumloop
Connectivity to Printify from Gumloop is made possible through CData Connect AI's Remote MCP Server. To interact with Printify data from Gumloop, we start by creating and configuring a Printify connection in CData Connect AI.
- Log into Connect AI, click Sources, and then click Add Connection
- Select "Printify" from the Add Connection panel
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Enter the necessary authentication properties to connect to Printify.
Start by setting the Profile connection property to the location of the Printify Profile on disk (e.g. C:\profiles\Profile.apip). Next, set the ProfileSettings connection property to the connection string for Printify (see below).
Printify API Profile Settings
In order to authenticate to Printify, you'll need to provide your API Key. To get your API Key navigate to My Profile, then Connections. In the Connections section you will be able to generate your Personal Access Token (API Key) and set your Token Access Scopes. Personal Access Tokens are valid for one year. An expired Personal Access Token can be re-generated using the same steps after it expires. Set the API Key to your Personal Access Token in the ProfileSettings property to connect.
- Click Save & Test
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Navigate to the Permissions tab in the Add Printify 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.
- Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
- On the Settings page, go to the Access Tokens section and click Create PAT.
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Give the PAT a name and click Create.
- The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.
With the Printify connection configured and a PAT generated, Gumloop is prepared to connect to Printify 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.
- Sign in to Gumloop and create an account
- Visit the Gumloop Credentials page to configure MCP server
- Click on Add Credentials and search and select MCP Server
- Provide the following details:
- URL: https://mcp.cloud.cdata.com/mcp
- Label: A descriptive name such as Printify-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 Printify data with Gumloop
- Visit Gumloop Personal workspace and click on the Create Flow
- Select the icon or press Ctrl + B to add a node or a subflow
- Search for Ask AI and select it
- Click Show More Options and enable the Connect MCP Server? option
- From the MCP Servers dropdown, choose the saved MCP credential
- Add a Prompt and Choose an AI Model according to your requirements
- After configuring the required details, Click Run to run the pipeline
With the workflow run completed, Gumloop demonstrates successful retrieval of Printify 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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