fastmcp_client_llm.py

"""
Author: Alex Punnen
Status:  Demo
Note: I have hosted the server in Hugging Face Spaces.
https://huggingface.co/spaces/alexcpn/mcpserver-demo/tree/main
With the Docker file it is running at "https://alexcpn-mcpserver-demo.hf.space:7860/mcp/"
This is a simple client to call the MCP server.
"""
import asyncio
from fastmcp import Client
from openai import OpenAI
from dotenv import load_dotenv
import os
import json

async def example():
    #async with Client("http://127.0.0.1:7860/mcp/") as client:
    async with Client("https://alexcpn-mcpserver-demo.hf.space/mcp/") as client:
        await client.ping()
        # List available tools
        tools = await client.list_tools()
        print("Available tools:", tools)
        tool_result = await client.call_tool("add", {"a": "1", "b": "2"})
        print("Tool result:", tool_result)
        # lets give this context to  an LLM to generate a prompt
        
        # Load the .env file and get the API key
        load_dotenv()
        api_key = os.getenv("OPENAI_API_KEY")

        # Initialize OpenAI client with OpenAI's official base URL
        openai_client = OpenAI(
            api_key=api_key,
            base_url="https://api.openai.com/v1"
        )
        a = 123124522
        b= 865734234
        question = f"Using the tools available {tools} frame the JSON RPC call to the tool add with a={a} and b={b}, do not add anything else to the output" + \
            "here is the JSON RPC call format {{\"method\": \"<method name>\", \"params\": {{\"<param 1 name>\": {<param 1 value>}, \"<param 2 name>\": {<param 2 value>} etc }}}}"
        

        # Use a simple model like gpt-3.5-turbo
        completion = openai_client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "user", "content":question }
            ]
        )
        tool_call = json.loads(completion.choices[0].message.content)
        # Print the response
        print("LLM response:", tool_call)
        print(tool_call["method"], tool_call["params"])
        
        # call the tool with LLM response
        tool_result = await client.call_tool(tool_call["method"], tool_call["params"])
        print("Tool result:", tool_result)
        # lets give this context to  an LLM to generate a prompt
        
        

        



if __name__ == "__main__":
    asyncio.run(example())