실무 패턴 & 안티패턴

from typing import Literal
from pydantic import BaseModel, Field

# ❌ 안티패턴 — 너무 느슨한 스키마
@mcp.tool()
def bad_search(params: dict) -> str: ...

# ✅ 패턴 — 구체적인 타입과 제약
@mcp.tool()
def search_products(
    query: str = Field(description="검색 키워드", min_length=1, max_length=200),
    category: Literal["electronics", "clothing", "books"] = "electronics",
    max_price: float = Field(default=100.0, ge=0, le=10000),
    page: int = Field(default=1, ge=1, le=100),
) -> list[dict]:
    """상품을 검색합니다. category는 electronics/clothing/books 중 하나."""
    ...

import time
from functools import lru_cache

_cache: dict = {}
CACHE_TTL = 300  # 5분

def cached_resource(key: str, loader):
    entry = _cache.get(key)
    if entry and time.time() - entry["ts"] < CACHE_TTL:
        return entry["data"]
    data = loader()
    _cache[key] = {"data": data, "ts": time.time()}
    return data

@mcp.resource("db://schema")
def db_schema() -> str:
    """DB 스키마 (5분 캐시)."""
    return cached_resource("db_schema", lambda: fetch_db_schema())

from mcp.server import Server
from mcp.types import ProgressNotification

server = Server("my-server")

@server.call_tool()
async def long_analysis(name, arguments, ctx):
    total_steps = 5
    for i in range(total_steps):
        # 진행 상황 알림 전송
        await ctx.session.send_progress_notification(
            progress_token=arguments.get("_progressToken"),
            progress=i + 1,
            total=total_steps
        )
        await process_step(i)
    return [{"type": "text", "text": "분석 완료"}]