from pydantic import BaseModel, Field
from typing import Literal, Optional, Annotated
from mcp.server.fastmcp import FastMCP
from openai import OpenAI
from pydantic_extra_types.timezone_name import TimeZoneName
from pydantic import BaseModel
import os
mcp = FastMCP(
name="OpenAI Web Search",
instructions="This MCP server provides access to OpenAI's websearch functionality through the Model Context Protocol."
)
class UserLocation(BaseModel):
type: Literal["approximate"] = "approximate"
city: str
country: str = None
region: str = None
timezone: TimeZoneName
@mcp.tool(
name="openai_web_search",
description="""OpenAI Web Search with reasoning models.
For quick multi-round searches: Use 'gpt-5-mini' with reasoning_effort='low' for fast iterations.
For deep research: Use 'gpt-5' with reasoning_effort='medium' or 'high'.
The result is already multi-round reasoned, so agents don't need continuous iterations.
Supports: gpt-4o (no reasoning), gpt-5/gpt-5-mini/gpt-5-nano, o3/o4-mini (with reasoning).""",
)
def openai_web_search(
input: Annotated[str, Field(description="The search query or question to search for")],
model: Annotated[Optional[Literal["gpt-4o", "gpt-4o-mini", "gpt-5", "gpt-5-mini", "gpt-5-nano", "o3", "o4-mini"]],
Field(description="AI model to use. Defaults to OPENAI_DEFAULT_MODEL env var or gpt-5-mini")] = None,
reasoning_effort: Annotated[Optional[Literal["low", "medium", "high", "minimal"]],
Field(description="Reasoning effort level for supported models (gpt-5, o3, o4-mini). Default: low for gpt-5-mini, medium for others")] = None,
type: Annotated[Literal["web_search_preview", "web_search_preview_2025_03_11"],
Field(description="Web search API version to use")] = "web_search_preview",
search_context_size: Annotated[Literal["low", "medium", "high"],
Field(description="Amount of context to include in search results")] = "medium",
user_location: Annotated[Optional[UserLocation],
Field(description="Optional user location for localized search results")] = None,
) -> str:
# 从环境变量读取默认模型，如果没有则使用 gpt-5-mini
if model is None:
model = os.getenv("OPENAI_DEFAULT_MODEL", "gpt-5-mini")
client = OpenAI()
# 判断是否为推理模型
reasoning_models = ["gpt-5", "gpt-5-mini", "gpt-5-nano", "o3", "o4-mini"]
# 构建请求参数
request_params = {
"model": model,
"tools": [
{
"type": type,
"search_context_size": search_context_size,
"user_location": user_location.model_dump() if user_location else None,
}
],
"input": input,
}
# 对推理模型设置智能默认值
if model in reasoning_models:
if reasoning_effort is None:
# gpt-5-mini 默认使用 low，其他推理模型默认 medium
if model == "gpt-5-mini":
reasoning_effort = "low" # 快速搜索
else:
reasoning_effort = "medium" # 深度研究
request_params["reasoning"] = {"effort": reasoning_effort}
response = client.responses.create(**request_params)
return response.output_text