Build Agents in Relevance AI with Access to Live Google Cloud Storage Data via CData Connect AI

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 Google Cloud Storage 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 Google Cloud Storage connectivity in Connect AI, register Connect AI in Relevance AI, and build an agent that interacts with live Google Cloud Storage data.

Step 1: Configure Google Cloud Storage Connectivity for Relevance AI

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

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

2. Select Google Cloud Storage from the Add Connection panel

3. Enter the necessary authentication properties to connect to Google Cloud Storage.

   Authenticate with a User Account

   You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

   When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

   Authenticate with a Service Account

   Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.

   You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:

   • InitiateOAuth: Set this to GETANDREFRESH.
   • OAuthJWTCertType: Set this to "PFXFILE".
   • OAuthJWTCert: Set this to the path to the .p12 file you generated.
   • OAuthJWTCertPassword: Set this to the password of the .p12 file.
   • OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
   • OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
   • OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
   • ProjectId: Set this to the Id of the project you want to connect to.

   The OAuth flow for a service account then completes.

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

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 Google Cloud Storage connection configured and a PAT generated, Relevance AI can now connect to Google Cloud Storage 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.

1. Sign in to Relevance AI and create an account if you do not already have one
2. From the sidebar, navigate to Agents and then click on New Agent

3. Select Build from scratch and name the agent (eg; CData MCP Server)

4. Inside the agent editor, select Advanced and then switch to the MCP Server tab

5. Click + Add Remote MCP Tools
6. 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)

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 Google Cloud Storage Data

1. Switch to the Run tab for your agent
2. Enter a task for example, "List the five most recent incidents from ServiceNow"

3. The agent will query Connect AI via the MCP endpoint and display live results from Google Cloud Storage data

With the connection complete, Relevance AI agents can now issue queries, retrieve records, and perform AI-driven tasks over live Google Cloud Storage data through CData Connect AI MCP Server.

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