How to Connect Flowise AI Agents to Live SQL Analysis Services 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 hundreds of enterprise data sources. Through its Model Context Protocol (MCP) server, CData Connect AI bridges Flowise agents with live SQL Analysis Services 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 SQL Analysis Services 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 SQL Analysis Services data in real time.

Step 1: Configure SQL Analysis Services Connectivity for Flowise

Connectivity to SQL Analysis Services from Flowise AI is made possible through CData Connect AI's Remote MCP Server. To interact with SQL Analysis Services data from Flowise 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
👁 Adding a connection in Connect AI

2. Select SQL Analysis Services from the Add Connection panel
👁 Selecting data source

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.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab and update user-based permissions
👁 Updating permissions

Once the connection is established, SQL Analysis Services 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
👁 Creating a new PAT

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, Flowise AI can now connect to SQL Analysis Services data through Connect AI.

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
👁 Click Add credential

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

Add the PAT variable

1. Navigate to Variables and Add Variable
👁 Navigate to Variables

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
👁 Adding PAT as variable in Flowise

Step 3: Build the agent in Flowise AI

1. Go to Agent Flows, select Add New
👁 Agent Flow page

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

3. Connect the Start node to the Agent node
👁 Connecting agent nodes in Flowise

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

👁 Fill in the details for Agent Node

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}}"
 }
}

👁 Configuring custom MCP tool in Flowise

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 SQL Analysis Services data in Flowise

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

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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