Integrating LangChain with HubDB Data via CData Connect AI

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 HubDB data in real time.

CData Connect AI offers a secure, low-code environment to connect HubDB 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 HubDB connectivity in CData Connect AI, register the MCP server with LangChain, and build a workflow that queries HubDB data in real time.

Prerequisites

• An account in CData Connect AI
• Python version 3.10 or higher, to install the LangChain and LangGraph packages
• Generate and save an OpenAI API key
• Install Visual Studio Code in your system

Step 1: Configure HubDB Connectivity for LangChain

Before LangChain can access HubDB, a HubDB 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 HubDB
3. Enter the necessary authentication properties to connect to HubDB

   There are two authentication methods available for connecting to HubDB data source: OAuth Authentication with a public HubSpot application and authentication with a Private application token.

   Using a Custom OAuth App

   AuthScheme must be set to "OAuth" in all OAuth flows. Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).

   Follow the steps below to register an application and obtain the OAuth client credentials:

   1. Log into your HubSpot app developer account.
      • Note that it must be an app developer account. Standard HubSpot accounts cannot create public apps.
   2. On the developer account home page, click the Apps tab.
   3. Click Create app.
   4. On the App info tab, enter and optionally modify values that are displayed to users when they connect. These values include the public application name, application logo, and a description of the application.
   5. On the Auth tab, supply a callback URL in the "Redirect URLs" box.
      • If you're creating a desktop application, set this to a locally accessible URL like http://localhost:33333.
      • If you are creating a Web application, set this to a trusted URL where you want users to be redirected to when they authorize your application.
   6. Click Create App. HubSpot then generates the application, along with its associated credentials.
   7. On the Auth tab, note the Client ID and Client secret. You will use these later to configure the driver.

   8. Under Scopes, select any scopes you need for your application's intended functionality.

      A minimum of the following scopes is required to access tables:

      • hubdb
      • oauth
      • crm.objects.owners.read
   9. Click Save changes.
   10. Install the application into a production portal with access to the features that are required by the integration.
       • Under "Install URL (OAuth)", click Copy full URL to copy the installation URL for your application.
       • Navigate to the copied link in your browser. Select a standard account in which to install the application.
       • Click Connect app. You can close the resulting tab.

   Using a Private App

   To connect using a HubSpot private application token, set the AuthScheme property to "PrivateApp."

   You can generate a private application token by following the steps below:

   1. In your HubDB account, click the settings icon (the gear) in the main navigation bar.
   2. In the left sidebar menu, navigate to Integrations > Private Apps.
   3. Click Create private app.
   4. On the Basic Info tab, configure the details of your application (name, logo, and description).
   5. On the Scopes tab, select Read or Write for each scope you want your private application to be able to access.
   6. A minimum of hubdb and crm.objects.owners.read is required to access tables.
   7. After you are done configuring your application, click Create app in the top right.
   8. Review the info about your application's access token, click Continue creating, and then Show token.
   9. Click Copy to copy the private application token.

   To connect, set PrivateAppToken to the private application token you retrieved.

   
4. Click Save & Test
5. Once authenticated, open the Permissions tab in the HubDB 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 HubDB connection configured and a PAT generated, LangChain is prepared to connect to HubDB 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 HubDB1?" #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 HubDB 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 HubDB?")
4. Accordingly, the agent responds with the results

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