Tool Calling

Tool calling in Responses API uses an item-based model. Use it when the model needs live data, application logic, or side effects that do not exist in the prompt itself.

Define Tools

from openai import OpenAI

client = OpenAI(
    base_url="https://api.naga.ac/v1",
    api_key="YOUR_API_KEY",
)

response = client.responses.create(
    model="gpt-4.1",
    input="Check the weather in Prague and tell me if I need a coat.",
    tools=[
        {
            "type": "function",
            "name": "lookup_weather",
            "description": "Look up current weather for a city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string"}
                },
                "required": ["city"],
            },
            "strict": True,
        }
    ],
    tool_choice="auto",
)

print(response.output)

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://api.naga.ac/v1',
  apiKey: 'YOUR_API_KEY',
});

const response = await client.responses.create({
  model: 'gpt-4.1',
  input: 'Check the weather in Prague and tell me if I need a coat.',
  tools: [
    {
      type: 'function',
      name: 'lookup_weather',
      description: 'Look up current weather for a city.',
      parameters: {
        type: 'object',
        properties: {
          city: { type: 'string' },
        },
        required: ['city'],
      },
      strict: true,
    },
  ],
  tool_choice: 'auto',
});

console.log(response.output);

curl https://api.naga.ac/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4.1",
    "input": "Check the weather in Prague and tell me if I need a coat.",
    "tools": [
      {
        "type": "function",
        "name": "lookup_weather",
        "description": "Look up current weather for a city.",
        "parameters": {
          "type": "object",
          "properties": {
            "city": { "type": "string" }
          },
          "required": ["city"]
        },
        "strict": true
      }
    ],
    "tool_choice": "auto"
  }'

Use tool_choice: "auto" for normal model-driven behavior. Force a specific tool only when your application really needs it.

Workflow

Define the tool in the request

Send one or more function definitions in tools[] and usually leave tool_choice on "auto".

Inspect the model's function call

Read the returned function_call item from output[] and parse its arguments payload.

Execute the tool in your application

Run the requested function using your own business logic, external API call, or internal system lookup.

Send the tool result back

Continue with a follow-up request that includes a function_call_output item using the same call_id.

Read the final answer

After the model receives the tool result, inspect the next response for the user-facing answer or any additional tool calls.

Model Tool Call Output

The model can return a function_call item in the output array:

{
  "type": "function_call",
  "call_id": "call_1",
  "name": "lookup_weather",
  "arguments": "{\"city\":\"Prague\"}",
  "status": "completed"
}

Send Tool Results Back

You return the tool result in a follow-up request using function_call_output:

{
  "model": "gpt-4.1",
  "input": [
    {
      "type": "function_call",
      "call_id": "call_1",
      "name": "lookup_weather",
      "arguments": "{\"city\":\"Prague\"}"
    },
    {
      "type": "function_call_output",
      "call_id": "call_1",
      "output": "{\"temperature_c\":7,\"raining\":true}"
    }
  ]
}

If you are continuing a longer workflow, also include any earlier context your app wants the model to keep.

Streaming Tool Arguments

When streaming, arguments arrive incrementally via response.function_call_arguments.delta events. Buffer those deltas until the matching .done event arrives. Some reasoning-capable models also emit a reasoning item before or alongside the tool call. If you want continuity across tool turns, replay that reasoning item unchanged in the follow-up request. See Responses Reasoning.

Common mistakes

forcing a tool when the model should be allowed to choose naturally
sending the tool result back in a chat-style role: tool message instead of a function_call_output item
parsing tool arguments before the streamed argument payload is complete
exposing tools that can cause side effects without application-level authorization checks

Get Started

Build

API Guides

Account

Product

Define Tools

Workflow

Model Tool Call Output

Send Tool Results Back

Streaming Tool Arguments

Common mistakes

​Define Tools

​Workflow

​Model Tool Call Output

​Send Tool Results Back

​Streaming Tool Arguments

​Common mistakes

​Related Docs

Define Tools

Workflow

Model Tool Call Output

Send Tool Results Back

Streaming Tool Arguments

Common mistakes

Related Docs