mcp_client.py

import os
import datetime
import gradio as gr
import openf1_tools
from smolagents import InferenceClientModel, LiteLLMModel, ToolCallingAgent, MCPClient

# Can manully set this to a specific time to make the agent think it is in the past
time = datetime.datetime.now().astimezone().isoformat() 

SYSTEM_PROMPT = """You are a helpful Formula 1 assistant and strategist. You have access to various F1 data and tools to help answer questions about races, drivers, teams, and more. 
Be concise and accurate in your responses. If you don't know something, use the available tools to find the information.
In addition, you will be asked to act as a live race engineer strategist during a Formula 1 race, making crucial calls during the event.

Current time (ISO 8601): {time}"""


def agent_chat(message: str, history: list):

    # Manually compose messages: system prompt, then history, then current user message
    message = f"{SYSTEM_PROMPT}\n{"\n".join([f"{x['role']}: {x['content']}" for x in history])}\nTask: {message}"
    return agent.run(message, max_steps=5)


if __name__ == "__main__":

    list_tools = True # Set to True to only list tools (used for debugging)
    local_model = True # If you have Ollama installed, set this to True
    openf1_tool_only = True
    
    try:

        mcp_client = MCPClient(
            {"url": "https://agents-mcp-hackathon-f1-mcp-server.hf.space/gradio_api/mcp/sse", "transport": "sse"})
        tools = mcp_client.get_tools()

        if openf1_tool_only:
            openf1_fn_names = [f"f1_mcp_server_{fn}" for fn in dir(openf1_tools) if callable(getattr(openf1_tools, fn))]
            openf1_fn_names.remove("f1_mcp_server_urlopen")
            tools = [t for t in tools if (t.name in openf1_fn_names)]

        if list_tools:
            print("### MCP tools ### ")
            print("\n".join(f"Tool {1+i}: {t.name}: {t.description}" for i,t in enumerate(tools)))
            mcp_client.disconnect()
            exit(0)


        # Define model
        if local_model:
            model = LiteLLMModel(
                model_id="ollama_chat/qwen3:1.7b",
                api_base="http://127.0.0.1:11434", # Default ollama server
                num_ctx=32768,
            )
        else:
            model = InferenceClientModel(
                model_id="deepseek-ai/DeepSeek-R1",
                provider="nebius",
                api_key=os.getenv("NEBIUS_API_KEY")
            )

        agent = ToolCallingAgent(model=model, tools=[*tools])


        chat_interface = gr.ChatInterface(
            fn=agent_chat,
            type="messages",
            examples=[
                "What are the driver standings for the 2024 Formula 1 season?",
                "What is the calendar for the 2024 Formula 1 season?"
            ],
            title="🏎️ Formula 1 Assistant",
            description="This is a simple agent that uses MCP tools to answer questions about Formula 1."
        )

        chat_interface.launch()

    finally:
        mcp_client.disconnect()