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

Step 1: Configure SingleStore Connectivity for Gumloop

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

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

2. Select "SingleStore" from the Add Connection panel

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

   The following connection properties are required in order to connect to data.

   • Server: The host name or IP of the server hosting the SingleStore database.
   • Port: The port of the server hosting the SingleStore database.
   • Database (Optional): The default database to connect to when connecting to the SingleStore Server. If this is not set, tables from all databases will be returned.

   Connect Using Standard Authentication

   To authenticate using standard authentication, set the following:

   • User: The user which will be used to authenticate with the SingleStore server.
   • Password: The password which will be used to authenticate with the SingleStore server.

   Connect Using Integrated Security

   As an alternative to providing the standard username and password, you can set IntegratedSecurity to True to authenticate trusted users to the server via Windows Authentication.

   Connect Using SSL Authentication

   You can leverage SSL authentication to connect to SingleStore data via a secure session. Configure the following connection properties to connect to data:

   • SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
   • SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
   • SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
   • SSLClientCertType: The certificate type of the client store.
   • SSLServerCert: The certificate to be accepted from the server.

   Connect Using SSH Authentication

   Using SSH, you can securely login to a remote machine. To access SingleStore data via SSH, configure the following connection properties:

   • SSHClientCert: Set this to the name of the certificate store for the client certificate.
   • SSHClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
   • SSHClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
   • SSHClientCertType: The certificate type of the client store.
   • SSHPassword: The password that you use to authenticate with the SSH server.
   • SSHPort: The port used for SSH operations.
   • SSHServer: The SSH authentication server you are trying to authenticate against.
   • SSHServerFingerPrint: The SSH Server fingerprint used for verification of the host you are connecting to.
   • SSHUser: Set this to the username that you use to authenticate with the SSH server.
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add SingleStore 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 SingleStore connection configured and a PAT generated, Gumloop is prepared to connect to SingleStore 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 SingleStore-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 SingleStore 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 SingleStore 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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