Integrating LangChain with Typeform 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 Typeform data in real time.

CData Connect AI offers a secure, low-code environment to connect Typeform 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 Typeform connectivity in CData Connect AI, register the MCP server with LangChain, and build a workflow that queries Typeform 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 Typeform Connectivity for LangChain

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

   Start by setting the Profile connection property to the location of the TypeForm Profile on disk (e.g. C:\profiles\TypeForm.apip). Next, set the ProfileSettings connection property to the connection string for TypeForm (see below).

   TypeForm API Profile Settings

   Authentication to TypeForm uses the OAuth standard.

   To authenticate to TypeForm, you must first register and configure an OAuth application with TypeForm here: https://admin.typeform.com/account#/section/tokens. Your app will be assigned a client ID and a client secret which can be set in the connection string. More information on setting up an OAuth application can be found at https://developer.typeform.com/get-started/.

   Note that there are several different use scenarios which all require different redirect URIs:

   • CData Desktop Applications: CData desktop applications (Sync, API Server, ArcESB) accept OAuth tokens at /src/oauthCallback.rst. The host and port is the same as the default port used by the application. For example, if you use http://localhost:8019/ to access CData Sync then the redirect URI will be http://localhost:8019/src/oauthCallback.rst.
   • CData Cloud Applications: CData cloud applications are similar to their desktop counterparts. If you access Connect AI at https://1.2.3.4/ then you should use the redirect https://1.2.3.4/src/oauthCallback.rst.
   • Desktop Application: When using a desktop application, the URI https://localhost:33333 is recommended.
   • Web Application: When developing a web application using the driver, use your own URI here such as https://my-website.com/oauth.

   After setting the following connection properties, you are ready to connect:

   • AuthScheme: Set this to OAuth.
   • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
   • OAuthClientId: Set this to the Client Id that is specified in your app settings.
   • OAuthClientSecret: Set this to Client Secret that is specified in your app settings.
   • CallbackURL: Set this to the Redirect URI you specified in your app settings.
   
4. Click Save & Test
5. Once authenticated, open the Permissions tab in the Typeform 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 Typeform connection configured and a PAT generated, LangChain is prepared to connect to Typeform 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 API1?" #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 Typeform 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 Typeform?")
4. Accordingly, the agent responds with the results

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