Create two Python files within the folder: config.py and langchain.py.
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In config.py, create a class Config to define your MCP server authentication and URL. You need to provide your Base64-encoded 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
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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 OpenAPI Key here. This can be found here: https://platform.openai.com/. ) agent = create_react_agent(llm, all_mcp_tools) user_prompt = "Tell me how many sales I had in Q1 for the current fiscal year." #Change prompts as per need print(f"\nUser 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())
