Integrating LlamaIndex with Cvent Data via CData Connect AI

Leverage the CData Connect AI Remote MCP Server to enable LlamaIndex ReAct agents to securely access and act on Cvent data in real time.

LlamaIndex is a data framework for building LLM applications — agents, RAG pipelines, and structured workflows that reason over external data. By integrating LlamaIndex with CData Connect AI through the built-in MCP Server, your agents can discover and query live Cvent data as native tools without writing custom connectors.

CData Connect AI offers a secure, low-code environment to connect Cvent and other data sources, removing the need for complex ETL and enabling seamless automation across business applications with live data.

This article outlines how to configure Cvent connectivity in CData Connect AI, register the MCP server with LlamaIndex, and build a ReAct agent that queries Cvent data in real time.

Prerequisites

Step 1: Configure Cvent Connectivity for LlamaIndex

Before LlamaIndex can access Cvent, a Cvent connection must be created in CData Connect AI. This connection is then exposed to LlamaIndex through the remote MCP server.

  1. Log in to Connect AI, click Sources, and then click + Add Connection
  2. From the available data sources, choose Cvent
  3. Enter the necessary authentication properties to connect to Cvent

    Before you can authenticate to Cvent, you must create a workspace and an OAuth application.

    Creating a Workspace

    To create a workspace:

    1. Sign into Cvent and navigate to App Switcher (the blue button in the upper right corner of the page) >> Admin.
    2. In the Admin menu, navigate to Integrations >> REST API.
    3. A new tab launches for Developer Management. Click on Manage API Access in the new tab.
    4. Create a Workspace and name it. Select the scopes you would like your developers to have access to. Scopes control what data domains the developer can access.
      • Choose All to allow developers to choose any scope, and any future scopes added to the REST API.
      • Choose Custom to limit the scopes developers can choose for their OAuth apps to selected scopes. To access all tables exposed by the driver, you need to set the following scopes:
        event/attendees:readevent/attendees:writeevent/contacts:read
        event/contacts:writeevent/custom-fields:readevent/custom-fields:write
        event/events:readevent/events:writeevent/sessions:delete
        event/sessions:readevent/sessions:writeevent/speakers:delete
        event/speakers:readevent/speakers:writebudget/budget-items:read
        budget/budget-items:writeexhibitor/exhibitors:readexhibitor/exhibitors:write
        survey/surveys:readsurvey/surveys:write

    Creating an OAuth Application

    After you have set up a Workspace and invited them, developers can sign up and create a custom OAuth app. See the Creating a Custom OAuth Application section in the Help documentation for more information.

    Connecting to Cvent

    After creating an OAuth application, set the following connection properties to connect to Cvent:

    • InitiateOAuth: GETANDREFRESH. Used to automatically get and refresh the OAuthAccessToken.
    • OAuthClientId: The Client ID associated with the OAuth application. You can find this on the Applications page in the Cvent Developer Portal.
    • OAuthClientSecret: The Client secret associated with the OAuth application. You can find this on the Applications page in the Cvent Developer Portal.
  4. Click Save & Test
  5. Once authenticated, open the Permissions tab in the Cvent connection and configure user-based permissions as required

Generate a Personal Access Token (PAT)

LlamaIndex authenticates to Connect AI using an account email and a Personal Access Token (PAT). Creating separate PATs for each integration is recommended to maintain access control granularity.

  1. In Connect AI, select the Gear icon in the top-right to open Settings
  2. Under Access Tokens, select Create PAT
  3. Provide a descriptive name for the token and select Create
  4. Copy the token and store it securely. The PAT will only be visible during creation

With the Cvent connection configured and a PAT generated, LlamaIndex is prepared to connect to Cvent data through the CData MCP server.

Step 2: Connect to the MCP server in LlamaIndex

To connect LlamaIndex with CData Connect AI Remote MCP Server and use OpenAI for reasoning, configure your MCP server endpoint and authentication in a 

config.py
file. These values let LlamaIndex’s MCP tool spec call the MCP server tools, while OpenAI handles the natural language reasoning.

  1. Create a folder for the LlamaIndex MCP project
  2. Create two Python files within the folder: 
    config.py
    and 
    llamaindex_agent.py
  3. In 
    config.py
    , define your MCP server URL and your Base64-encoded CData Connect AI email and PAT (obtained in the prerequisites): 
    class Config:

      MCP_BASE_URL = "https://mcp.cloud.cdata.com/mcp"   # MCP Server URL
      MCP_AUTH     = "base64encoded(EMAIL:PAT)"          # Base64 encoded Connect AI Email:PAT

    Note: You can create the base64 encoded version of MCP_AUTH using any Base64 encoding tool.

  4. In 
    llamaindex_agent.py
    , wire up the MCP tool spec and a ReAct agent: 
    """
Integrates a LlamaIndex ReAct agent with the CData Connect AI MCP server.
The script discovers MCP tools, wraps them as LlamaIndex tools, and runs an
agent loop driven by OpenAI for reasoning.
"""

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import ReActAgent
from llama_index.llms.openai import OpenAI
from config import Config

async def main():

    # Initialize the MCP client pointed at Connect AI
    mcp_client = BasicMCPClient(
        Config.MCP_BASE_URL,
        headers={"Authorization": f"Basic {Config.MCP_AUTH}"},
    )

    # Discover tools the MCP server exposes (getCatalogs, queryData, etc.)
    tool_spec = McpToolSpec(client=mcp_client)
    tools = await tool_spec.to_tool_list_async()
    print("Discovered MCP tools:", [t.metadata.name for t in tools])

    # Configure the LLM that drives the ReAct loop
    llm = OpenAI(
        model="gpt-4o",
        temperature=0.2,
        api_key="YOUR_OPENAI_API_KEY",  # https://platform.openai.com/
    )

    # Build the agent with the MCP-backed tools
    agent = ReActAgent(tools=tools, llm=llm)

    user_prompt = "How many tables are available in Cvent1?"  # Change as needed
    print(f"
User prompt: {user_prompt}")

    response = await agent.run(user_prompt)

    print("Agent final response:", response)

if __name__ == "__main__":
    asyncio.run(main())

Step 3: Install the LlamaIndex packages

Since this workflow uses LlamaIndex together with the CData Connect AI MCP server and OpenAI for reasoning, install the required Python packages.

Run the following command in your project terminal:

pip install llama-index llama-index-tools-mcp llama-index-llms-openai

Step 4: Prompt Cvent using LlamaIndex (via the MCP server)

  1. When the installation finishes, run 
    python llamaindex_agent.py
    to execute the script
  2. The script connects to the MCP server and discovers the CData Connect AI MCP tools available for querying your connected data
  3. Supply a prompt (e.g., "How many tables are available in Cvent?")
  4. The agent reasons over the available tools, calls 
    queryData
    against Cvent, and responds with the result

Get CData Connect AI

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

Ready to get started?

Learn more about CData Connect AI or sign up for free trial access:

Free Trial

In this article

Related articles