FastMCP

from datetime import datetime
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("example-server")

@mcp.tool()
def get_current_time() -> str:
    """Get the current time."""
    return f"current_time: {datetime.now().isoformat()}"

@mcp.tool()
def get_weather(city: str, metric: str = "celsius") -> str:
    """Get weather info for a city."""
    return f"It is 23 {metric} in {city}"

if __name__ == "__main__":
    mcp.run(transport="stdio")

import os
from openai import OpenAI
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

client = OpenAI(
    base_url="https://api.sambanova.ai/v1/",
    api_key=os.environ["SAMBANOVA_API_KEY"],
)

server_params = StdioServerParameters(
    command="python", args=["example_server.py"]
)

# Inside an async function:
#   async with stdio_client(server_params) as (read, write):
#       async with ClientSession(read, write) as session:
#           await session.initialize()
#           tools = await session.list_tools()
#           # Convert MCP tool schemas to OpenAI function-calling format,
#           # then call client.chat.completions.create(
#           #     model="Meta-Llama-3.3-70B-Instruct",
#           #     messages=[...],
#           #     tools=tools_schemas,
#           # )
#           # and dispatch tool calls via session.call_tool(...).