Integrate LibreChat with Live Zuora Data via CData Connect AI

Yazhini G
Yazhini G
Technical Marketing Engineer
Leverage the CData Connect AI Remote MCP Server to enable LibreChat to securely access and query live Zuora data from within the chat interface.

LibreChat is an open-source, self-hosted AI chat platform that brings together multiple LLM providers, agents, and assistants behind a single interface. It also supports the Model Context Protocol (MCP), so you can connect external tools and data sources directly to the chat and pull in live data from the systems you already work with.

By integrating LibreChat with CData Connect AI through the built-in MCP Server, LibreChat gains governed, real-time access to live Zuora data. This enables you to list catalogs, explore schemas, and query records from Zuora data using natural language prompts, with all data access running securely against authorized sources.

This article explains how to configure Zuora connectivity in Connect AI, generate the required personal access token, install LibreChat, register the Connect AI MCP Server, configure an LLM provider, and verify the integration by querying live Zuora data from the LibreChat interface.

Step 1: Configure Zuora connectivity for LibreChat

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

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

    Zuora uses the OAuth standard to authenticate users. See the online Help documentation for a full OAuth authentication guide.

    Configuring Tenant property

    In order to create a valid connection with the provider you need to choose one of the Tenant values (USProduction by default) which matches your account configuration. The following is a list with the available options:

    • USProduction: Requests sent to https://rest.zuora.com.
    • USAPISandbox: Requests sent to https://rest.apisandbox.zuora.com"
    • USPerformanceTest: Requests sent to https://rest.pt1.zuora.com"
    • EUProduction: Requests sent to https://rest.eu.zuora.com"
    • EUSandbox: Requests sent to https://rest.sandbox.eu.zuora.com"

    Selecting a Zuora Service

    Two Zuora services are available: Data Query and AQuA API. By default ZuoraService is set to AQuADataExport.

    DataQuery

    The Data Query feature enables you to export data from your Zuora tenant by performing asynchronous, read-only SQL queries. We recommend to use this service for quick lightweight SQL queries.

    Limitations
    • The maximum number of input records per table after filters have been applied: 1,000,000
    • The maximum number of output records: 100,000
    • The maximum number of simultaneous queries submitted for execution per tenant: 5
    • The maximum number of queued queries submitted for execution after reaching the limitation of simultaneous queries per tenant: 10
    • The maximum processing time for each query in hours: 1
    • The maximum size of memory allocated to each query in GB: 2
    • The maximum number of indices when using Index Join, in other words, the maximum number of records being returned by the left table based on the unique value used in the WHERE clause when using Index Join: 20,000

    AQuADataExport

    AQuA API export is designed to export all the records for all the objects ( tables ). AQuA query jobs have the following limitations:

    Limitations
    • If a query in an AQuA job is executed longer than 8 hours, this job will be killed automatically.
    • The killed AQuA job can be retried three times before returned as failed.
  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 LibreChat. 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 Zuora connection configured and a PAT generated, LibreChat can now connect to Zuora data through Connect AI.

Step 2: Install LibreChat and configure Connect AI MCP

Next, install LibreChat locally and configure the Connect AI Remote MCP Server so that the chat interface can discover and call live data tools through Connect AI.

  1. Install LibreChat by following the official installation guide. If you are using the npm setup, make sure MongoDB and MeiliSearch are installed and running locally
  2. Once the installation is complete, start LibreChat and open http://localhost:3080/ in your browser to access the chat interface
  3. In the left navigation bar, click the MCP Settings icon, then click Add MCP
  4. In the Add MCP panel, configure the server with the following values:
    • Name: CData MCP, or any name of your choice
    • Description: Optional description for the server
    • MCP Server URL: https://mcp.cloud.cdata.com/mcp
    • Transport: Streamable HTTPS
    • Authentication: API Key
    • Header Format: Basic
    • API Key: base64-encoded value of email:PAT

    Note: LibreChat will use Basic authentication with Connect AI. Combine your Connect AI user email and the PAT you created earlier in the format email:PAT, then base64 encode the combined string and paste it in the API Key field. For example, [email protected]:ABC123...XYZ789 base64-encoded becomes something like: dXNlckBkb21haW4uY29tOkFCQzEyMy4uLlhZWjc4OQ==

  5. Check I trust this application and click Add to save the server
  6. The CData MCP server now appears in the left navigation bar. Click the connect icon next to it to establish the connection to Connect AI

Enable the MCP server and configure an LLM provider

LibreChat requires at least one LLM provider to power the chat. Enable the MCP server in the chat input and add an API key for your preferred provider so the model can interpret prompts and call MCP tools through Connect AI.

  1. In the chat interface, click the MCP selector at the bottom of the input box and confirm that CData MCP is checked so the tools are exposed to the chat
  2. At the top of the chat, click the model selector and choose your preferred LLM provider (e.g., OpenAI, Anthropic, Google) and model
  3. Click Set API Key next to the chosen provider, paste your provider API key, and click Submit

With the MCP server and an LLM provider configured, LibreChat is ready to query live Zuora data through Connect AI.

Step 3: Query live Zuora data from LibreChat

With the integration complete, use the LibreChat chat input to interact with live Zuora data through natural language prompts handled by the configured LLM.

  1. With the CData MCP server enabled and a model selected, type a prompt in the chat input, for example:
    • List all catalogs in my cdata mcp
    • Show the available schemas and tables for Zuora
    • Query the top 5 records from a table in Zuora data
  2. LibreChat calls the Connect AI MCP Server and returns live results from Zuora data

At this point, your LibreChat instance communicates with the Connect AI MCP Server and retrieves live Zuora data through remote MCP tools directly from the chat interface.

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