Build Agents in Relevance AI with Access to Live SQL Analysis Services 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 SQL Analysis Services 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 SQL Analysis Services connectivity in Connect AI, register Connect AI in Relevance AI, and build an agent that interacts with live SQL Analysis Services data.

Step 1: Configure SQL Analysis Services Connectivity for Relevance AI

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

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

2. Select SQL Analysis Services from the Add Connection panel

3. Enter the necessary authentication properties to connect to SQL Analysis Services.

   To connect, provide authentication and set the Url property to a valid SQL Server Analysis Services endpoint. You can connect to SQL Server Analysis Services instances hosted over HTTP with XMLA access. See the Microsoft documentation to configure HTTP access to SQL Server Analysis Services.

   To secure connections and authenticate, set the corresponding connection properties, below. The data provider supports the major authentication schemes, including HTTP and Windows, as well as SSL/TLS.

   • HTTP Authentication

     Set AuthScheme to "Basic" or "Digest" and set User and Password. Specify other authentication values in CustomHeaders.

   • Windows (NTLM)

     Set the Windows User and Password and set AuthScheme to "NTLM".

   • Kerberos and Kerberos Delegation

     To authenticate with Kerberos, set AuthScheme to NEGOTIATE. To use Kerberos delegation, set AuthScheme to KERBEROSDELEGATION. If needed, provide the User, Password, and KerberosSPN. By default, the data provider attempts to communicate with the SPN at the specified Url.

   • SSL/TLS:

     By default, the data provider attempts to negotiate SSL/TLS by checking the server's certificate against the system's trusted certificate store. To specify another certificate, see the SSLServerCert property for the available formats.

   You can then access any cube as a relational table: When you connect the data provider retrieves SSAS metadata and dynamically updates the table schemas. Instead of retrieving metadata every connection, you can set the CacheLocation property to automatically cache to a simple file-based store.

   See the Getting Started section of the CData documentation, under Retrieving Analysis Services Data, to execute SQL-92 queries to the cubes.

   
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 SQL Analysis Services connection configured and a PAT generated, Relevance AI can now connect to SQL Analysis Services 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 SQL Analysis Services 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 SQL Analysis Services data

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

Get CData Connect AI

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