工具调用
在 Chat Completions、Responses 或 Anthropic Messages 上让模型调用你的函数。
你在请求里描述自己的函数。模型需要用某个函数时，不会直接给出最终回答，而是返回函数名和 JSON 参数。你的代码执行函数、把结果发回去，模型再接着回答。模型自己从不执行任何东西。
工具只需按你正在用的端点格式写一次：Chat Completions、Responses 或 Anthropic Messages。同一套定义适用于该端点上的所有模型，所以把一段工具调用循环从 GPT 换到 Claude 或 GLM，只需改一行模型名。整个循环都可以用 ohmygpt/mock 免费测试。
Chat Completions
1. 发送工具定义
curl https://api.ohmygpt.com/v1/chat/completions \
-H "Authorization: Bearer $OHMYGPT_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-5.4-nano",
"messages": [{"role": "user", "content": "What is the weather in Paris?"}],
"tools": [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a city.",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"]
}
}
}]
}'模型返回 finish_reason: "tool_calls"，以及它要发起的调用（节选）：
{
"choices": [
{
"index": 0,
"finish_reason": "tool_calls",
"message": {
"role": "assistant",
"content": null,
"tool_calls": [
{
"id": "call_wwN71lk5akb6aFGYQv9f234b",
"type": "function",
"function": { "name": "get_weather", "arguments": "{\"city\":\"Paris\"}" }
}
]
}
}
]
}arguments 是 JSON 字符串，使用前先解析。
2. 回传结果
先把 assistant 消息原样追加到对话里，再为每个调用追加一条 tool 消息，带上对应的 tool_call_id，然后带着同样的 tools 重新发送整段对话。Python 完整示例：
import json
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OHMYGPT_API_KEY"], base_url="https://api.ohmygpt.com/v1")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a city.",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string", "description": "City name, e.g. Paris"}},
"required": ["city"],
},
},
}
]
def get_weather(city: str) -> dict:
return {"city": city, "temperature_c": 18, "condition": "cloudy"}
messages = [{"role": "user", "content": "What's the weather in Paris?"}]
response = client.chat.completions.create(model="openai/gpt-5.4-nano", messages=messages, tools=tools)
message = response.choices[0].message
messages.append(message)
for call in message.tool_calls or []:
args = json.loads(call.function.arguments)
messages.append({"role": "tool", "tool_call_id": call.id, "content": json.dumps(get_weather(**args))})
final = client.chat.completions.create(model="openai/gpt-5.4-nano", messages=messages, tools=tools)
print(final.choices[0].message.content)In **Paris**, it’s currently **cloudy** with a temperature of **18°C**.同一段程序把模型换成 model="anthropic/claude-haiku-4-5"，输出为 The weather in Paris is currently **cloudy** with a temperature of **18°C** (about 64°F).
控制工具调用
tool_choice | 效果 |
|---|---|
"auto"（默认） | 由模型决定是否调用工具 |
"none" | 模型直接用文本回答 |
"required" | 模型必须至少调用一个工具 |
{"type": "function", "function": {"name": "get_time"}} | 模型必须调用 get_time |
免费测试整个循环
ohmygpt/mock 的回答是确定的。请求里带有工具时，它会调用第一个工具（或 tool_choice 指定的那个），所有必填参数都填对应类型的零值；收到工具结果后，再回复一段文本。把上面第一个请求的模型换成 "model": "ohmygpt/mock"，返回：
{
"id": "chatcmpl-ohmygpt-mock",
"object": "chat.completion",
"model": "mock",
"choices": [
{
"index": 0,
"finish_reason": "tool_calls",
"message": {
"role": "assistant",
"content": null,
"tool_calls": [
{
"id": "call_mock_1",
"type": "function",
"function": { "name": "get_weather", "arguments": "{\"city\":\"\"}" }
}
]
}
}
],
"usage": { "prompt_tokens": 73, "completion_tokens": 8, "total_tokens": 81 }
}账户完成充值后即可使用 mock 模型。
Responses
工具定义是扁平的（name 和 parameters 与 type 并列）。模型的调用以 function_call 条目出现在 output 里；把结果作为 function_call_output 条目发回，并带上相同的 call_id：
curl https://api.ohmygpt.com/v1/responses \
-H "Authorization: Bearer $OHMYGPT_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "ohmygpt/mock",
"input": [
{"role": "user", "content": "What is the weather in Paris?"},
{"type": "function_call", "call_id": "call_mock_1", "name": "get_weather", "arguments": "{\"city\":\"Paris\"}"},
{"type": "function_call_output", "call_id": "call_mock_1", "output": "{\"temperature_c\":18,\"condition\":\"cloudy\"}"}
],
"tools": [{
"type": "function",
"name": "get_weather",
"description": "Get the current weather for a city.",
"parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}
}]
}'第一轮返回的 output 条目如下：
{ "type": "function_call", "id": "fc_0", "call_id": "call_mock_1", "name": "get_weather", "arguments": "{\"city\":\"\"}", "status": "completed" }Anthropic Messages
工具定义使用 input_schema。模型的调用是一个 tool_use 内容块，同时返回 stop_reason: "tool_use"；在下一条 user 消息里用 tool_result 块发回结果，并带上相同的 ID：
curl https://api.ohmygpt.com/v1/messages \
-H "x-api-key: $OHMYGPT_API_KEY" \
-H "anthropic-version: 2023-06-01" \
-H "Content-Type: application/json" \
-d '{
"model": "ohmygpt/mock",
"max_tokens": 1024,
"messages": [
{"role": "user", "content": "What is the weather in Paris?"},
{"role": "assistant", "content": [{"type": "tool_use", "id": "call_mock_1", "name": "get_weather", "input": {"city": "Paris"}}]},
{"role": "user", "content": [{"type": "tool_result", "tool_use_id": "call_mock_1", "content": "{\"temperature_c\":18,\"condition\":\"cloudy\"}"}]}
],
"tools": [{
"name": "get_weather",
"description": "Get the current weather for a city.",
"input_schema": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}
}]
}'第一轮返回的内容块如下：
{ "type": "tool_use", "id": "call_mock_1", "name": "get_weather", "input": { "city": "" } }流式输出
设置 "stream": true 后，调用会分段到达：先是函数名，然后是一段段字符串形式的参数片段（Chat Completions 上是 tool_calls[].function.arguments 增量，Responses 上是 response.function_call_arguments.delta 事件，Messages 上是 input_json_delta 事件）。把片段拼接起来，等流结束后再解析 JSON。