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

Integrate Flowise AI with the CData Connect AI MCP Server to enable agents to securely query and act on live data without replication.

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 Snowflake 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 Snowflake 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 Snowflake data in real time.

About Snowflake Data Integration

CData simplifies access and integration of live Snowflake data. Our customers leverage CData connectivity to:

  • Reads and write Snowflake data quickly and efficiently.
  • Dynamically obtain metadata for the specified Warehouse, Database, and Schema.
  • Authenticate in a variety of ways, including OAuth, OKTA, Azure AD, Azure Managed Service Identity, PingFederate, private key, and more.

Many CData users use CData solutions to access Snowflake from their preferred tools and applications, and replicate data from their disparate systems into Snowflake for comprehensive warehousing and analytics.

For more information on integrating Snowflake with CData solutions, refer to our blog: https://www.cdata.com/blog/snowflake-integrations.


Getting Started


Step 1: Configure Snowflake Connectivity for Flowise

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

  1. Log into Connect AI, click Sources, and then click Add Connection
  2. Select Snowflake from the Add Connection panel
  3. Enter the necessary authentication properties to connect to Snowflake.

    To connect to Snowflake:

    1. Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
    2. Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
    3. Set Warehouse to the Snowflake warehouse.
    4. (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
    5. (Optional) Set Database and Schema to restrict the tables and views exposed.

    See the Getting Started guide in the CData driver documentation for more information.

  4. Click Save & Test
  5. Navigate to the Permissions tab and update user-based permissions

Once the connection is established, Snowflake 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 Snowflake connection configured and a PAT generated, Flowise AI can now connect to Snowflake 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 Snowflake data in Flowise

  1. Open the Chat tab in Flowise
  2. Type a query such as "Show top 10 records from Snowflake 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.

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