For AI agents: markdown of this page — /docs-content-en/ai/chat/tools.md documentation index — /llms.txt

Function calling (tools)

Describe the functions the model may call, and it decides on its own when a call is needed. The model does not run the function — it returns its name and arguments, and your code performs the call. You send the result back into the conversation, and the model composes the final answer.

When the model decides to call a function, finish_reason is tool_calls and content is null. The arguments arrive in tool_calls[].function.arguments as a JSON string.

Function description

Terminal 
curl -X POST https://vibecode.bitrix24.com/v1/chat/completions \
  -H "X-Api-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "bitrix/bitrixgpt-5.5",
    "messages": [
      {"role": "user", "content": "What is the weather in Berlin?"}
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get the current weather in the specified city",
          "parameters": {
            "type": "object",
            "properties": {
              "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
          }
        }
      }
    ],
    "tool_choice": "auto"
  }'

Response example

JSON 
{
  "id": "chatcmpl-9aaee7576d8057ab",
  "object": "chat.completion",
  "model": "bitrix/bitrixgpt-5.5",
  "choices": [
    {
      "index": 0,
      "finish_reason": "tool_calls",
      "message": {
        "role": "assistant",
        "content": null,
        "tool_calls": [
          {
            "id": "chatcmpl-tool-a423212c4e614cab",
            "type": "function",
            "function": {
              "name": "get_weather",
              "arguments": "{\"city\": \"Berlin\"}"
            }
          }
        ]
      }
    }
  ],
  "usage": {"prompt_tokens": 275, "completion_tokens": 26, "total_tokens": 301}
}

Returning the result to the conversation

Run the function on your side and add a message with the tool role and the same tool_call_id to messages:

JSON 
{
  "messages": [
    {"role": "user", "content": "What is the weather in Berlin?"},
    {"role": "assistant", "content": null, "tool_calls": [{"id": "chatcmpl-tool-a423212c4e614cab", "type": "function", "function": {"name": "get_weather", "arguments": "{\"city\": \"Berlin\"}"}}]},
    {"role": "tool", "tool_call_id": "chatcmpl-tool-a423212c4e614cab", "content": "+5°C, cloudy"}
  ]
}

In its next response the model composes human-readable text based on the returned result.

Errors

HTTP Code Description
400 unsupported_tool_type The tools array contains an element with a type other than function. For web search use POST /v1/search
400 tool_choice_without_tools tool_choice was passed, but the tools array is absent

The full list of common API errors — Errors.

Known specifics

Only type: "function" is supported. The endpoint has no built-in tools such as web search. A request with a different type value is rejected with 400 rather than silently executed without tools.

Names in the response are checked against your list. If the model invents a function name that is not in tools, that call is removed from the response. When all calls turn out to be invented, finish_reason switches to stop and a textual explanation arrives in content — the conversation does not loop on a nonexistent function.

Arguments arrive as a string. The arguments field is JSON as a string, and you must parse it before use. The model can return syntactically valid JSON that does not match your parameter schema, so validate the values before the call.

See also