Introduction to Agentic AI
Model Context Protocol (MCP)
Model Context Protocol why MCP
Model Context Protocol MCP
- 2024.11 Anthropic 發佈之開放標準
- 讓LLM可以整合external tools, systems, and data sources
Model Context Protocol component
- MCP Client
AI 助理。當判斷需要外部協助時，它會作為 MCP 用戶端運行。 - MCP Server
可自行建置，或使用現有的軟體。
託管特定的工具或資源（如搜尋文件、存取 API、讀取文件、執行自訂提示的功能）。 - MCP Protocol
MCP 定義Client端如何向伺服器要求資訊或操作，以及伺服器如何回應。可確保相容性和安全性。
Model Context Protocol mcp server
from fastmcp import FastMCP
# Initialize the server with a name
mcp = FastMCP("my-first-server")
# Define a tool using the @mcp.tool decorator
@mcp.tool
def get_weather(city: str) -> dict:
"""Get the current weather for a city."""
# In production, you'd call a real weather API
# For now, we'll return mock data
weather_data = {
"new york": {"temp": 72, "condition": "sunny"},
"london": {"temp": 59, "condition": "cloudy"},
"tokyo": {"temp": 68, "condition": "rainy"},
}
city_lower = city.lower()
if city_lower in weather_data:
return {"city": city, **weather_data[city_lower]}
else:
return {"city": city, "temp": 70, "condition": "unknown"}
# Run the server
if __name__ == "__main__":
mcp.run(transport="stdio")pip install fastmcpmy_server.py
Model Context Protocol mcp server
-
FastMCP("my-first-server") creates your server with a name
-
@mcp.tool is the decorator that turns any function into an MCP tool
-
The docstring becomes the tool’s description (LLMs use this to understand when to call it)
-
Type hints (city: str, -> dict) tell MCP the expected inputs and outputs
-
transport="stdio" means the server communicates via standard input/output (perfect for local testing)
Model Context Protocol mcp client
import asyncio
from fastmcp import Client
async def main():
# Point the client at your server file
client = Client("my_server.py")
# Connect to the server
async with client:
# List available tools
tools = await client.list_tools()
print("Available tools:")
for tool in tools:
print(f" - {tool.name}: {tool.description}")
print("\n" + "="*50 + "\n")
# Call the weather tool
result = await client.call_tool(
"get_weather",
{"city": "Tokyo"}
)
print(f"Weather result: {result}")
if __name__ == "__main__":
asyncio.run(main())test_client.py
Model Context Protocol mcp server
-
Client("my_server.py") tells the client which server to connect to
-
async with client: handles the connection lifecycle automatically
-
list_tools() discovers what tools are available (this is MCP's dynamic discovery)
-
call_tool("get_weather", {"city": "Tokyo"}) invokes the tool with parameters
Model Context Protocol more tools
from fastmcp import FastMCP
from datetime import datetime
mcp = FastMCP("my-first-server")
@mcp.tool
def get_weather(city: str) -> dict:
"""Get the current weather for a city."""
weather_data = {
"new york": {"temp": 72, "condition": "sunny"},
"london": {"temp": 59, "condition": "cloudy"},
"tokyo": {"temp": 68, "condition": "rainy"},
}
city_lower = city.lower()
if city_lower in weather_data:
return {"city": city, **weather_data[city_lower]}
return {"city": city, "temp": 70, "condition": "unknown"}
@mcp.tool
def get_time(timezone: str = "UTC") -> str:
"""Get the current time in a specified timezone."""
# Simplified - in production use pytz or zoneinfo
return f"Current time ({timezone}): {datetime.now().strftime('%H:%M:%S')}"
@mcp.tool
def calculate(expression: str) -> dict:
"""Safely evaluate a mathematical expression."""
try:
# Only allow safe math operations
allowed_chars = set("0123456789+-*/.() ")
if not all(c in allowed_chars for c in expression):
return {"error": "Invalid characters in expression"}
result = eval(expression) # Safe because we validated input
return {"expression": expression, "result": result}
except Exception as e:
return {"error": str(e)}
if __name__ == "__main__":
mcp.run(transport="stdio")my_server.py
Model Context Protocol production
# Run the server
if __name__ == "__main__":
mcp.run(transport="http", host="0.0.0.0", port=8000)# Run the server
if __name__ == "__main__":
mcp.run(transport="stdio")my_server.py
my_server.py
測試環境
正式環境