在生成模型响应或构建智能体时，您可以使用内置工具、函数调用、工具搜索和远程 MCP 服务器来扩展其能力。这些工具使模型能够搜索网络、从您的文件中进行检索、在运行时加载延迟的工具定义、调用您自己的函数或访问第三方服务。仅限 gpt-5.4 and later models support tool_search.
网络搜索
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import OpenAI from "openai";
const client = new OpenAI();
const response = await client.responses.create({
model: "gpt-5.5",
tools: [
{ type: "web_search" },
],
input: "What was a positive news story from today?",
});
console.log(response.output_text);
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from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-5.5",
tools=[{"type": "web_search"}],
input="What was a positive news story from today?"
)
print(response.output_text)
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curl "https://api.openai.com/v1/responses" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-5.5",
"tools": [{"type": "web_search"}],
"input": "what was a positive news story from today?"
}'
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openai responses create \
--model gpt-5.5 \
--raw-output \
--transform 'output.#(type=="message").content.0.text' <<'YAML'
tools:
- type: web_search
input: What was a positive news story from today?
YAML
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using OpenAI.Responses;
string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.5", apiKey: key);
ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateWebSearchTool());
OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
ResponseItem.CreateUserMessageItem([
ResponseContentPart.CreateInputTextPart("What was a positive news story from today?"),
]),
], options);
Console.WriteLine(response.GetOutputText());
文件搜索
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from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-5.5",
input="What is deep research by OpenAI?",
tools=[{
"type": "file_search",
"vector_store_ids": ["<vector_store_id>"]
}]
)
print(response)
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import OpenAI from "openai";
const openai = new OpenAI();
const response = await openai.responses.create({
model: "gpt-5.5",
input: "What is deep research by OpenAI?",
tools: [
{
type: "file_search",
vector_store_ids: ["<vector_store_id>"],
},
],
});
console.log(response);
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using OpenAI.Responses;
string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.5", apiKey: key);
ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateFileSearchTool(["<vector_store_id>"]));
OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
ResponseItem.CreateUserMessageItem([
ResponseContentPart.CreateInputTextPart("What is deep research by OpenAI?"),
]),
], options);
Console.WriteLine(response.GetOutputText());
工具搜索
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from openai import OpenAI
client = OpenAI()
crm_namespace = {
"type": "namespace",
"name": "crm",
"description": "CRM tools for customer lookup and order management.",
"tools": [
{
"type": "function",
"name": "get_customer_profile",
"description": "Fetch a customer profile by customer ID.",
"parameters": {
"type": "object",
"properties": {
"customer_id": {"type": "string"},
},
"required": ["customer_id"],
"additionalProperties": False,
},
},
{
"type": "function",
"name": "list_open_orders",
"description": "List open orders for a customer ID.",
"defer_loading": True,
"parameters": {
"type": "object",
"properties": {
"customer_id": {"type": "string"},
},
"required": ["customer_id"],
"additionalProperties": False,
},
},
],
}
response = client.responses.create(
model="gpt-5.5",
input="List open orders for customer CUST-12345.",
tools=[
crm_namespace,
{"type": "tool_search"},
],
parallel_tool_calls=False,
)
print(response.output)
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import OpenAI from "openai";
const client = new OpenAI();
const crmNamespace = {
type: "namespace",
name: "crm",
description: "CRM tools for customer lookup and order management.",
tools: [
{
type: "function",
name: "get_customer_profile",
description: "Fetch a customer profile by customer ID.",
parameters: {
type: "object",
properties: {
customer_id: { type: "string" },
},
required: ["customer_id"],
additionalProperties: false,
},
},
{
type: "function",
name: "list_open_orders",
description: "List open orders for a customer ID.",
defer_loading: true,
parameters: {
type: "object",
properties: {
customer_id: { type: "string" },
},
required: ["customer_id"],
additionalProperties: false,
},
},
],
};
const response = await client.responses.create({
model: "gpt-5.5",
input: "List open orders for customer CUST-12345.",
tools: [crmNamespace, { type: "tool_search" }],
parallel_tool_calls: false,
});
console.log(response.output);
函数调用
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import OpenAI from "openai";
const client = new OpenAI();
const tools = [
{
type: "function",
name: "get_weather",
description: "Get current temperature for a given location.",
parameters: {
type: "object",
properties: {
location: {
type: "string",
description: "City and country e.g. Bogotá, Colombia",
},
},
required: ["location"],
additionalProperties: false,
},
strict: true,
},
];
const response = await client.responses.create({
model: "gpt-5",
input: [
{ role: "user", content: "What is the weather like in Paris today?" },
],
tools,
});
console.log(response.output[0].to_json());
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from openai import OpenAI
client = OpenAI()
tools = [
{
"type": "function",
"name": "get_weather",
"description": "Get current temperature for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and country e.g. Bogotá, Colombia",
}
},
"required": ["location"],
"additionalProperties": False,
},
"strict": True,
},
]
response = client.responses.create(
model="gpt-5",
input=[
{"role": "user", "content": "What is the weather like in Paris today?"},
],
tools=tools,
)
print(response.output[0].to_json())
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using System.Text.Json;
using OpenAI.Responses;
string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5", apiKey: key);
ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateFunctionTool(
functionName: "get_weather",
functionDescription: "Get current temperature for a given location.",
functionParameters: BinaryData.FromObjectAsJson(new
{
type = "object",
properties = new
{
location = new
{
type = "string",
description = "City and country e.g. Bogotá, Colombia"
}
},
required = new[] { "location" },
additionalProperties = false
}),
strictModeEnabled: true
)
);
OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
ResponseItem.CreateUserMessageItem([
ResponseContentPart.CreateInputTextPart("What is the weather like in Paris today?")
])
], options);
Console.WriteLine(JsonSerializer.Serialize(response.OutputItems[0]));
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curl -X POST https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5",
"input": [
{"role": "user", "content": "What is the weather like in Paris today?"}
],
"tools": [
{
"type": "function",
"name": "get_weather",
"description": "Get current temperature for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and country e.g. Bogotá, Colombia"
}
},
"required": ["location"],
"additionalProperties": false
},
"strict": true
}
]
}'
远程 MCP
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curl https://api.openai.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-5.5",
"tools": [
{
"type": "mcp",
"server_label": "dmcp",
"server_description": "A Dungeons and Dragons MCP server to assist with dice rolling.",
"server_url": "https://dmcp-server.deno.dev/sse",
"require_approval": "never"
}
],
"input": "Roll 2d4+1"
}'
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import OpenAI from "openai";
const client = new OpenAI();
const resp = await client.responses.create({
model: "gpt-5.5",
tools: [
{
type: "mcp",
server_label: "dmcp",
server_description: "A Dungeons and Dragons MCP server to assist with dice rolling.",
server_url: "https://dmcp-server.deno.dev/sse",
require_approval: "never",
},
],
input: "Roll 2d4+1",
});
console.log(resp.output_text);
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from openai import OpenAI
client = OpenAI()
resp = client.responses.create(
model="gpt-5.5",
tools=[
{
"type": "mcp",
"server_label": "dmcp",
"server_description": "A Dungeons and Dragons MCP server to assist with dice rolling.",
"server_url": "https://dmcp-server.deno.dev/sse",
"require_approval": "never",
},
],
input="Roll 2d4+1",
)
print(resp.output_text)
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using OpenAI.Responses;
string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.5", apiKey: key);
ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateMcpTool(
serverLabel: "dmcp",
serverUri: new Uri("https://dmcp-server.deno.dev/sse"),
toolCallApprovalPolicy: new McpToolCallApprovalPolicy(GlobalMcpToolCallApprovalPolicy.NeverRequireApproval)
));
OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
ResponseItem.CreateUserMessageItem([
ResponseContentPart.CreateInputTextPart("Roll 2d4+1")
])
], options);
Console.WriteLine(response.GetOutputText());
以下是 OpenAI 平台中可用工具的概览——选择其中一个以获取更多使用指南。
调用自定义代码，赋予模型访问额外数据与功能的能力。
通过模型上下文协议 (MCP) 服务器为模型赋予新能力。
在托管的 Shell 环境中上传并复用版本化的技能包。
在托管的容器或您自己的本地运行时中执行 Shell 命令。
动态加载相关工具到模型上下文中，以优化 token 使用。
当发起请求生成 模型响应,你通常通过在以下位置指定配置来启用工具访问: tools 参数。每个工具都有其独特的配置要求——详情请参阅 可用工具 部分以获取详细说明。
基于提供的 提示词,模型会自动决定是否使用已配置的工具。例如,如果你的提示请求超出模型训练截止日期的信息且已启用网络搜索,模型通常会调用网络搜索工具以检索相关的最新信息。
一些高级工作流也可以在交互过程中加载更多工具定义。例如， 工具搜索 可以延迟函数定义，直到模型确定需要它们时才加载。
你可以通过设置 tool_choice 参数 in the API request.
在 Agents SDK 中，工具语义保持不变，但连接逻辑转移到了 Agent 定义和工作流设计中，而不是单一的 Responses API 请求。
- 当需要某个专家自行调用工具时，直接在 Agent 上附加托管工具、函数工具或托管 MCP 工具。
- 当管理器需要控制面向用户的回复时，将专家作为工具暴露。
- 即使 SDK 建模了工具决策，也要在运行时保留 shell、应用补丁和计算机使用工具链。
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import { tool } from "@openai/agents";
import { z } from "zod";
const getWeatherTool = tool({
name: "get_weather",
description: "Get the weather for a given city.",
parameters: z.object({ city: z.string() }),
async execute({ city }) {
return `The weather in ${city} is sunny.`;
},
});
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from agents import function_tool
@function_tool
def get_weather(city: str) -> str:
"""Get the weather for a given city."""
return f"The weather in {city} is sunny."
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import { Agent } from "@openai/agents";
const summarizer = new Agent({
name: "Summarizer",
instructions: "Generate a concise summary of the supplied text.",
});
const mainAgent = new Agent({
name: "Research assistant",
tools: [
summarizer.asTool({
toolName: "summarize_text",
toolDescription: "Generate a concise summary of the supplied text.",
}),
],
});
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from agents import Agent
summarizer = Agent(
name="Summarizer",
instructions="Generate a concise summary of the supplied text.",
)
main_agent = Agent(
name="Research assistant",
tools=[
summarizer.as_tool(
tool_name="summarize_text",
tool_description="Generate a concise summary of the supplied text.",
)
],
)
使用 智能体定义 当你正在构建单个专家时， 编排与交接 当工具影响所有权时， 护栏与人工审查 当工具影响审批时，以及 集成与可观测性 当功能来自 MCP 时。
