Integrating LangChain with Google Cloud Storage 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 Google Cloud Storage 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 Google Cloud Storage data in real time.

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

Prerequisites

Step 1: Configure Google Cloud Storage Connectivity for LangChain

Before LangChain can access Google Cloud Storage, a Google Cloud Storage 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 Google Cloud Storage
  3. Enter the necessary authentication properties to connect to Google Cloud Storage

    Authenticate with a User Account

    You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

    When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

    Authenticate with a Service Account

    Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.

    You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:

    • InitiateOAuth: Set this to GETANDREFRESH.
    • OAuthJWTCertType: Set this to "PFXFILE".
    • OAuthJWTCert: Set this to the path to the .p12 file you generated.
    • OAuthJWTCertPassword: Set this to the password of the .p12 file.
    • OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
    • OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
    • OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
    • ProjectId: Set this to the Id of the project you want to connect to.

    The OAuth flow for a service account then completes.

  4. Click Save & Test
  5. Once authenticated, open the Permissions tab in the Google Cloud Storage 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 Google Cloud Storage connection configured and a PAT generated, LangChain is prepared to connect to Google Cloud Storage 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 GoogleCloudStorage1?" #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 Google Cloud Storage 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 Google Cloud Storage?")
  4. Accordingly, the agent responds with the results

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