Relevance AI is an AI automation and agent-building platform that enables organizations to create autonomous workflows powered by natural language reasoning. Users can visually design agents that interact with APIs, databases, and third-party systems to complete everyday business tasks or data operations.
By integrating Relevance AI with CData Connect AI through the built-in MCP (Model Context Protocol) Server, your agents can query, summarize, and act on live Snowflake data in real time.
This connection bridges Relevance AI intelligent workflow engine with the governed enterprise connectivity of CData Connect AI ensuring every query runs securely against authorized sources without manual data export.
This article outlines the steps to configure Snowflake connectivity in Connect AI, register Connect AI in Relevance AI, and build an agent that interacts with live Snowflake data.
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 Relevance AI
Connectivity to Snowflake from Relevance AI is made possible through CData Connect AI's Remote MCP Server. To interact with Snowflake data from Relevance AI, we start by creating and configuring a Snowflake connection in CData Connect AI.
- Log into Connect AI, click Sources, and then click Add Connection
π Adding a connection in Connect AI
- Select Snowflake from the Add Connection panel
π Selecting data source
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Enter the necessary authentication properties to connect to Snowflake.
To connect to Snowflake:
- Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
- Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
- Set Warehouse to the Snowflake warehouse.
- (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
- (Optional) Set Database and Schema to restrict the tables and views exposed.
- (Optional) If MFA is enabled on your Snowflake account (via Duo Security), set MFACode to the passcode generated by your Duo authenticator app.
See the Getting Started guide in the CData driver documentation for more information.
π Configuring a connection (Salesforce is shown)
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Click Save & Test
- Navigate to the Permissions tab and update user-based permissions
π Updating permissions
Add a Personal Access Token
A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Relevance AI. It is best practice to create a separate PAT for each integration to maintain granular access control.
- Click the gear icon () at the top right of the Connect AI app to open Settings
- On the Settings page, go to the Access Tokens section and click Create PAT
- Give the PAT a descriptive name and click Create
π Creating a new PAT
- Copy the token when displayed and store it securely. It will not be shown again
With the Snowflake connection configured and a PAT generated, Relevance AI can now connect to Snowflake data through Connect AI.
Step 2: Configure Connectivity in Relevance AI
The CData Connect AI MCP endpoint and authorization details are registered within Relevance AI so that agents can call live data from Connect AI.
- Sign in to Relevance AI and create an account if you do not already have one
- From the sidebar, navigate to Agents and then click on New Agent
π Creating a new agent in Relevance AI
- Select Build from scratch and name the agent (eg; CData MCP Server)
π Building an agent from scratch
- Inside the agent editor, select Advanced and then switch to the MCP Server tab
π Opening MCP Server settings
- Click + Add Remote MCP Tools
- In the dialog that appears, fill out the fields as follows:
- URL: https://mcp.cloud.cdata.com/mcp
- Label: Any custom label (eg; cdata_mcp_server)
- Authentication: Select Custom headers
- Add header key:value pair. Combine your email and PAT as email:PAT and encode that string in Base64 and then prefix with the word Basic
- Key: Authorization
- Value: Basic base64(email:PAT)
π Connecting to CData Connect AI MCP Server in Relevance AI
Click Connect to establish the connection. Relevance AI will verify your credentials and register the CData Connect AI MCP Server for use in agents.
Step 3: Build and Run a Relevance AI Agent with Live Snowflake Data
- Switch to the Run tab for your agent
- Enter a task for example, "List the five most recent incidents from ServiceNow"
π Running the Relevance AI agent
- The agent will query Connect AI via the MCP endpoint and display live results from Snowflake data
π Example query result from Connect AI
With the connection complete, Relevance AI agents can now issue queries, retrieve records, and perform AI-driven tasks over live Snowflake data through CData Connect AI MCP Server.
Get CData Connect AI
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