How to Connect Flowise AI Agents to Live SingleStore Data via CData Connect AI

Flowise AI is an open-source, no-code tool for building AI workflows and custom agents visually. Its drag-and-drop interface allows you to integrate large language models (LLMs) with APIs, databases, and external systems effortlessly.

CData Connect AI enables real-time connectivity to over 350+ enterprise data sources. Through its Model Context Protocol (MCP) server, CData Connect AI bridges Flowise agents with live SingleStore securely and efficiently, no data replication required. By combining Flowise AI's intuitive agent builder with CData's MCP integration, users can create agents capable of fetching, analyzing, and acting upon live SingleStore data directly within Flowise AI workflows.

This guide shows you how to connect Flowise AI to CData Connect AI MCP, set up credentials, and enable your agents to query live SingleStore data in real time.

Step 1: Configure SingleStore Connectivity for Flowise

Connectivity to SingleStore from Flowise AI is made possible through CData Connect AI's Remote MCP Server. To interact with SingleStore data from Flowise AI, 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 and update user-based permissions

Once the connection is established, SingleStore data is now accessible in CData Connect AI and ready to be used with MCP enabled tools.

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Flowise AI. It is best practice to create a separate PAT for each integration to maintain granular access control.

1. Click the gear icon () at the top right of the Connect AI app to open Settings
2. On the Settings page, go to the Access Tokens section and click Create PAT
3. Give the PAT a descriptive name and click Create

4. Copy the token when displayed and store it securely. It will not be shown again

With the SingleStore connection configured and a PAT generated, Flowise AI can now connect to SingleStore data through the CData MCP Server.

Step 2: Configure Connect AI credentials in Flowise AI

Log in to Flowise AI workspace to set up the integration.

Add OpenAI credentials

1. Navigate to Credentials and choose Add Credential

2. Select OpenAI API from the dropdown
3. Provide a name (e.g., OpenAI_Key) and paste the API key

Add the PAT variable

1. Navigate to Variables and Add Variable

2. Set Variable Name (e.g., PAT), choose Static as type, and set the Value to Base64-encoded username:PAT
3. Click Add to save the variable

Step 3: Build the agent in Flowise AI

1. Go to Agent Flows, select Add New

2. Click the "+" icon to add a new node and choose Agent and drag the agent to the workflow

3. Connect the Start node to the Agent node

Configure agent settings

Double-click on the Agent node and fill in the details:

• Model: select ChatOpenAI or preferred model (e.g., gpt-4o-mini)
• Connect Credential: Select OpenAI API key credential which was created earlier
• Streaming: Enabled

Add the custom MCP tool

1. Under Tools, click Add Tool and choose Custom MCP
2. Fill in the JSON parameters as shown below:

 
{
  "url": "https://mcp.cloud.cdata.com/mcp",
  "headers": {
    "Authorization": "Basic {{$vars.PAT}}"
  }
}

Click the refresh icon to load available MCP actions. Once actions are listed, now Flowise agent is successfully connected to CData Connect AI MCP.

Step 4: Test and query live SingleStore data in Flowise

1. Open the Chat tab in Flowise
2. Type a query such as "Show top 10 records from SingleStore data table"
3. Observe that responses are fetched in real time via the CData Connect AI MCP connection

With the workflow run completed, Flowise demonstrates successful retrieval of Salesforce 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.

Get CData Connect AI

To access 300+ SaaS, Big Data, and NoSQL sources directly from your cloud applications, try CData Connect AI today!