LangChain

class Config:
    MCP_BASE_URL = "https://mcp.cloud.cdata.com/mcp"   #MCP Server URL
    MCP_AUTH = "OAUTH_JWT_TOKEN"   #Your Connect AI Embed OAuth JWT bearer token

"""
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"Bearer {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())

python langchain.py