Integrating LangChain with Cvent Data via CData Connect AI

Dibyendu Datta
Dibyendu Datta
Lead Technology Evangelist
Leverage the CData Connect AI Remote MCP Server to enable LangChain agents to securely access and act on Cvent data within automated workflows.

LangChain is a framework used by developers, data engineers, and AI practitioners for building AI-powered applications and workflows by combining reasoning models (LLMs), tools, APIs, and data connectors. By integrating LangChain with CData Connect AI through the built-in MCP Server, workflows can effortlessly access and interact with live Cvent data in real time.

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 LangChain, and build a workflow that queries Cvent data in real time.

Prerequisites

Step 1: Configure Cvent Connectivity for LangChain

Before LangChain can access Cvent, a Cvent connection must be created in CData Connect AI. This connection is then exposed to LangChain 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)

LangChain 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, LangChain is prepared to connect to Cvent data through the CData MCP server.

Note: You can also generate a PAT from LangChain in the Integrations section of Connect AI. Simply click Connect --> Create PAT to generate it.

Step 2: Connect to the MCP server in LangChain

To connect LangChain with CData Connect AI Remote MCP Server and use OpenAI (ChatGPT) for reasoning, you need to configure your MCP server endpoint and authentication values in a config.py file. These values allow LangChain to call the MCP server tools, while OpenAI handles the natural language reasoning.

  1. Create a folder for LangChain MCP
  2. Create two Python files within the folder: config.py and langchain.py
  3. In config.py, create a class Config to define your MCP server authentication and URL. You need to provide your Base64-encoded CData Connect AI username 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 langchain.py, set up your MCP server and MCP client to call the tools and prompts: 
     
""" 
Integrates a LangChain ReAct agent with CData Connect AI MCP server. 
The script demonstrates fetching, filtering, and using tools with an LLM for agent-based reasoning. 
""" 

import asyncio 
from langchain_mcp_adapters.client import MultiServerMCPClient 
from langchain_openai import ChatOpenAI 
from langgraph.prebuilt import create_react_agent 
from config import Config 

async def main(): 

# Initialize MCP client with one or more server URLs
mcp_client = MultiServerMCPClient( 
  connections={ 
    "default": { # you can name this anything 
    "transport": "streamable_http", 
    "url": Config.MCP_BASE_URL, 
    "headers": {"Authorization": f"Basic {Config.MCP_AUTH}"}, 
    } 
  } 
) 

# Load remote MCP tools exposed by the server 
all_mcp_tools = await mcp_client.get_tools() 
print("Discovered MCP tools:", [tool.name for tool in all_mcp_tools]) 

# Create and run the ReAct style agent 
llm = ChatOpenAI( 
  model="gpt-4o",  
  temperature=0.2, 
  api_key="YOUR_OPEN_API_KEY" #Use your OpenAI API Key here, this can be found here: https://platform.openai.com/ 
) 

agent = create_react_agent(llm, all_mcp_tools) 

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

# Send a prompt asking the agent to use the MCP tools 
response = await agent.ainvoke( 
  { "messages": [{ "role": "user", "content": (user_prompt),}]} 
) 

# Print out the agent's final response 
final_msg = response["messages"][-1].content 
print("Agent final response:", final_msg) 

if __name__ == "__main__": 

asyncio.run(main())

Step 3: Install the LangChain and LangGraph packages

Since this workflow uses LangChain together with CData Connect AI MCP and integrates OpenAI for reasoning, you need to install the required Python packages.

Run the following command in your project terminal:

 
pip install langchain-mcp-adapters langchain-openai langgraph

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

  1. When the installation finishes, run python langchain.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. Accordingly, the agent responds with the results

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