mcpapp/src/providers/openai_provider/tools.py

import json
import logging
from typing import Any

from openai import Stream
from openai.types.chat import ChatCompletion, ChatCompletionChunk
from openai.types.chat.chat_completion_message_tool_call import ChatCompletionMessageToolCall

logger = logging.getLogger(__name__)


def has_tool_calls(response: Stream[ChatCompletionChunk] | ChatCompletion) -> bool:
    """Checks if the OpenAI response contains tool calls."""
    try:
        if isinstance(response, ChatCompletion):
            if response.choices:
                return bool(response.choices[0].message.tool_calls)
            else:
                logger.warning("No choices found in OpenAI non-streaming response for tool check.")
                return False
        elif isinstance(response, Stream):
            logger.warning("has_tool_calls check on a stream is unreliable before consumption.")
            return False
        else:
            logger.warning(f"has_tool_calls received unexpected type: {type(response)}")
            return False
    except Exception as e:
        logger.error(f"Error checking for tool calls: {e}", exc_info=True)
        return False


def parse_tool_calls(response: ChatCompletion) -> list[dict[str, Any]]:
    """Parses tool calls from a non-streaming OpenAI response."""
    parsed_calls = []
    try:
        if not isinstance(response, ChatCompletion):
            logger.error(f"parse_tool_calls expects ChatCompletion, got {type(response)}")
            return []

        if not response.choices:
            logger.warning("No choices found in OpenAI non-streaming response for tool parsing.")
            return []

        tool_calls: list[ChatCompletionMessageToolCall] | None = response.choices[0].message.tool_calls
        if not tool_calls:
            return []

        logger.debug(f"Parsing {len(tool_calls)} tool calls from OpenAI response.")
        for call in tool_calls:
            if call.type == "function":
                parts = call.function.name.split("__", 1)
                if len(parts) == 2:
                    server_name, func_name = parts
                else:
                    logger.warning(f"Could not determine server_name from tool name '{call.function.name}'. Assuming default or error needed.")
                    server_name = None
                    func_name = call.function.name

                arguments_obj = None
                try:
                    if isinstance(call.function.arguments, str):
                        arguments_obj = json.loads(call.function.arguments)
                    else:
                        arguments_obj = call.function.arguments
                except json.JSONDecodeError as json_err:
                    logger.error(f"Failed to parse JSON arguments for tool {func_name} (ID: {call.id}): {json_err}")
                    logger.error(f"Raw arguments string: {call.function.arguments}")
                    arguments_obj = {"error": "Failed to parse arguments", "raw_arguments": call.function.arguments}

                parsed_calls.append({
                    "id": call.id,
                    "server_name": server_name,
                    "function_name": func_name,
                    "arguments": arguments_obj,
                })
            else:
                logger.warning(f"Unsupported tool call type: {call.type}")

        return parsed_calls
    except Exception as e:
        logger.error(f"Error parsing OpenAI tool calls: {e}", exc_info=True)
        return []


def format_tool_results(tool_call_id: str, result: Any) -> dict[str, Any]:
    """Formats a tool result for an OpenAI follow-up request."""
    try:
        if isinstance(result, dict):
            content = json.dumps(result)
        elif isinstance(result, str):
            content = result
        else:
            content = str(result)
    except Exception as e:
        logger.error(f"Error JSON-encoding tool result for {tool_call_id}: {e}")
        content = json.dumps({"error": "Failed to encode tool result", "original_type": str(type(result))})

    logger.debug(f"Formatting tool result for call ID {tool_call_id}")
    return {
        "role": "tool",
        "tool_call_id": tool_call_id,
        "content": content,
    }


def convert_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
    """Converts internal tool format to OpenAI's format."""
    openai_tools = []
    logger.debug(f"Converting {len(tools)} tools to OpenAI format.")
    for tool in tools:
        server_name = tool.get("server_name")
        tool_name = tool.get("name")
        description = tool.get("description")
        input_schema = tool.get("inputSchema")

        if not server_name or not tool_name or not description or not input_schema:
            logger.warning(f"Skipping invalid tool definition during conversion: {tool}")
            continue

        prefixed_tool_name = f"{server_name}__{tool_name}"

        openai_tool_format = {
            "type": "function",
            "function": {
                "name": prefixed_tool_name,
                "description": description,
                "parameters": input_schema,
            },
        }
        openai_tools.append(openai_tool_format)
        logger.debug(f"Converted tool: {prefixed_tool_name}")

    return openai_tools


def get_original_message_with_calls(response: ChatCompletion) -> dict[str, Any]:
    """Extracts the assistant's message containing tool calls."""
    try:
        if isinstance(response, ChatCompletion) and response.choices and response.choices[0].message.tool_calls:
            message = response.choices[0].message
            return message.model_dump(exclude_unset=True)
        else:
            logger.warning("Could not extract original message with tool calls from response.")
            return {"role": "assistant", "content": "[Could not extract tool calls message]"}
    except Exception as e:
        logger.error(f"Error extracting original message with calls: {e}", exc_info=True)
        return {"role": "assistant", "content": f"[Error extracting tool calls message: {str(e)}]"}