from __future__ import annotations

import asyncio
import copy
import functools
import hashlib
import inspect
import json
from collections import Counter
from collections.abc import Awaitable, Callable, Mapping
from dataclasses import dataclass
from types import MappingProxyType
from typing import TYPE_CHECKING, Any, Protocol, Union

import httpx
from typing_extensions import NotRequired, TypedDict

from .. import _debug
from .._mcp_tool_metadata import resolve_mcp_tool_description_for_model, resolve_mcp_tool_title
from ..exceptions import AgentsException, MCPToolCancellationError, ModelBehaviorError, UserError

try:
    from mcp.shared.exceptions import McpError as _McpError
except ImportError:  # pragma: no cover – mcp is optional on Python < 3.10
    _McpError = None  # type: ignore[assignment, misc]
from ..logger import logger
from ..run_context import RunContextWrapper
from ..strict_schema import ensure_strict_json_schema
from ..tool import (
    FunctionTool,
    Tool,
    ToolErrorFunction,
    ToolOrigin,
    ToolOriginType,
    ToolOutputImageDict,
    ToolOutputTextDict,
    _build_handled_function_tool_error_handler,
    _build_wrapped_function_tool,
    default_tool_error_function,
)
from ..tool_context import ToolContext
from ..tracing import FunctionSpanData, get_current_span, mcp_tools_span
from ..util._custom_data import maybe_extract_custom_data
from ..util._types import MaybeAwaitable

if TYPE_CHECKING:
    ToolOutputItem = ToolOutputTextDict | ToolOutputImageDict
    ToolOutput = str | ToolOutputItem | list[ToolOutputItem]
else:
    ToolOutputItem = Union[ToolOutputTextDict, ToolOutputImageDict]  # noqa: UP007
    ToolOutput = Union[str, ToolOutputItem, list[ToolOutputItem]]  # noqa: UP007

if TYPE_CHECKING:
    from mcp.types import Tool as MCPTool

    from ..agent import AgentBase
    from .server import MCPServer


_MCP_FUNCTION_TOOL_NAME_MAX_LENGTH = 64
_MCP_FUNCTION_TOOL_HASH_LENGTH = 8


@dataclass(frozen=True)
class _PrefixedToolNameCandidate:
    batch_key: tuple[int, int]
    base_name: str
    seed: str
    initial_name: str
    server_index: int
    tool_index: int


class HttpClientFactory(Protocol):
    """Protocol for HTTP client factory functions.

    This interface matches the MCP SDK's McpHttpClientFactory but is defined locally
    to avoid accessing internal MCP SDK modules.
    """

    def __call__(
        self,
        headers: dict[str, str] | None = None,
        timeout: httpx.Timeout | None = None,
        auth: httpx.Auth | None = None,
    ) -> httpx.AsyncClient: ...


@dataclass
class ToolFilterContext:
    """Context information available to tool filter functions."""

    run_context: RunContextWrapper[Any]
    """The current run context."""

    agent: AgentBase
    """The agent that is requesting the tool list."""

    server_name: str
    """The name of the MCP server."""


if TYPE_CHECKING:
    ToolFilterCallable = Callable[[ToolFilterContext, MCPTool], MaybeAwaitable[bool]]
else:
    ToolFilterCallable = Callable[[ToolFilterContext, Any], MaybeAwaitable[bool]]
"""A function that determines whether a tool should be available.

Args:
    context: The context information including run context, agent, and server name.
    tool: The MCP tool to filter.

Returns:
    Whether the tool should be available (True) or filtered out (False).
"""


class ToolFilterStatic(TypedDict):
    """Static tool filter configuration using allowlists and blocklists."""

    allowed_tool_names: NotRequired[list[str]]
    """Optional list of tool names to allow (whitelist).
    If set, only these tools will be available."""

    blocked_tool_names: NotRequired[list[str]]
    """Optional list of tool names to exclude (blacklist).
    If set, these tools will be filtered out."""


if TYPE_CHECKING:
    ToolFilter = ToolFilterCallable | ToolFilterStatic | None
else:
    ToolFilter = Union[ToolFilterCallable, ToolFilterStatic, None]  # noqa: UP007
"""A tool filter that can be either a function, static configuration, or None (no filtering)."""


@dataclass
class MCPToolMetaContext:
    """Context information available to MCP tool meta resolver functions."""

    run_context: RunContextWrapper[Any]
    """The current run context."""

    server_name: str
    """The name of the MCP server."""

    tool_name: str
    """The name of the tool being invoked."""

    arguments: dict[str, Any] | None
    """The parsed tool arguments."""


@dataclass(frozen=True)
class MCPToolCustomDataContext:
    """Context passed to MCP tool custom data extractors."""

    run_context: RunContextWrapper[Any]
    """The current run context."""

    server_name: str
    """The name of the MCP server."""

    tool_name: str
    """The original MCP tool name invoked on the server."""

    tool_display_name: str
    """The public tool name exposed through the Agents SDK."""

    arguments: Mapping[str, Any]
    """The parsed tool arguments."""

    result_meta: Mapping[str, Any] | None
    """The MCP tool result ``_meta`` payload, if present."""

    structured_content: Mapping[str, Any] | None
    """The MCP tool result ``structuredContent`` payload, if present."""

    is_error: bool | None
    """The MCP tool result ``isError`` flag, if present."""

    tool_output: ToolOutput
    """The model-visible tool output produced by the Agents SDK."""


if TYPE_CHECKING:
    MCPToolMetaResolver = Callable[
        [MCPToolMetaContext],
        MaybeAwaitable[dict[str, Any] | None],
    ]
    MCPToolCustomDataExtractor = Callable[
        [MCPToolCustomDataContext],
        MaybeAwaitable[Mapping[str, Any] | None],
    ]
else:
    MCPToolMetaResolver = Callable[..., Any]
    MCPToolCustomDataExtractor = Callable[..., Any]
"""A function that produces MCP request metadata for tool calls.

Args:
    context: Context information about the tool invocation.

Returns:
    A dict to send as MCP `_meta`, or None to omit metadata.
"""
"""A function that produces SDK-only custom data for MCP tool output items."""


def create_static_tool_filter(
    allowed_tool_names: list[str] | None = None,
    blocked_tool_names: list[str] | None = None,
) -> ToolFilterStatic | None:
    """Create a static tool filter from allowlist and blocklist parameters.

    This is a convenience function for creating a ToolFilterStatic.

    Args:
        allowed_tool_names: Optional list of tool names to allow (whitelist).
        blocked_tool_names: Optional list of tool names to exclude (blacklist).

    Returns:
        A ToolFilterStatic if any filtering is specified, None otherwise.
    """
    if allowed_tool_names is None and blocked_tool_names is None:
        return None

    filter_dict: ToolFilterStatic = {}
    if allowed_tool_names is not None:
        filter_dict["allowed_tool_names"] = allowed_tool_names
    if blocked_tool_names is not None:
        filter_dict["blocked_tool_names"] = blocked_tool_names

    return filter_dict


class MCPUtil:
    """Set of utilities for interop between MCP and Agents SDK tools."""

    @staticmethod
    def _extract_static_meta(tool: Any) -> dict[str, Any] | None:
        meta = getattr(tool, "meta", None)
        if isinstance(meta, dict):
            return copy.deepcopy(meta)

        model_extra = getattr(tool, "model_extra", None)
        if isinstance(model_extra, dict):
            extra_meta = model_extra.get("meta")
            if isinstance(extra_meta, dict):
                return copy.deepcopy(extra_meta)

        model_dump = getattr(tool, "model_dump", None)
        if callable(model_dump):
            dumped = model_dump()
            if isinstance(dumped, dict):
                dumped_meta = dumped.get("meta")
                if isinstance(dumped_meta, dict):
                    return copy.deepcopy(dumped_meta)

        return None

    @classmethod
    async def get_all_function_tools(
        cls,
        servers: list[MCPServer],
        convert_schemas_to_strict: bool,
        run_context: RunContextWrapper[Any],
        agent: AgentBase,
        failure_error_function: ToolErrorFunction | None = default_tool_error_function,
        include_server_in_tool_names: bool = False,
        reserved_tool_names: set[str] | None = None,
    ) -> list[Tool]:
        """Get all function tools from a list of MCP servers."""
        tools: list[Tool] = []
        tool_names: set[str] = set()

        if include_server_in_tool_names:
            server_tool_batches = []
            for server_index, server in enumerate(servers):
                listed_tools = await cls._list_tools_with_span(server, run_context, agent)
                server_tool_batches.append((server_index, server, listed_tools))

            prefixed_tool_name_overrides = cls._build_prefixed_tool_name_overrides(
                server_tool_batches,
                reserved_names=set(reserved_tool_names or set()),
            )

            for server_index, server, mcp_tools in server_tool_batches:
                tool_name_overrides = [
                    prefixed_tool_name_overrides[(server_index, tool_index)]
                    for tool_index in range(len(mcp_tools))
                ]
                function_tools = cls._convert_mcp_tools_to_function_tools(
                    mcp_tools,
                    server,
                    convert_schemas_to_strict,
                    agent,
                    failure_error_function=failure_error_function,
                    tool_name_overrides=tool_name_overrides,
                )
                server_tool_names = {tool.name for tool in function_tools}
                duplicate_tool_names = sorted(server_tool_names & tool_names)
                if duplicate_tool_names:
                    raise UserError(
                        "Duplicate tool names found across MCP servers: "
                        f"{', '.join(duplicate_tool_names)}"
                    )
                tool_names.update(server_tool_names)
                tools.extend(function_tools)

            return tools

        for server in servers:
            server_tools = await cls.get_function_tools(
                server,
                convert_schemas_to_strict,
                run_context,
                agent,
                failure_error_function=failure_error_function,
            )
            server_tool_names = {tool.name for tool in server_tools}
            duplicate_tool_names = sorted(server_tool_names & tool_names)
            if duplicate_tool_names:
                raise UserError(
                    "Duplicate tool names found across MCP servers: "
                    f"{', '.join(duplicate_tool_names)}. "
                    "Pass `include_server_in_tool_names=True` to "
                    "`MCPUtil.get_all_function_tools()` or set "
                    "`mcp_config={'include_server_in_tool_names': True}` on the "
                    "agent to prefix tool names with their server name and avoid "
                    "collisions."
                )
            tool_names.update(server_tool_names)
            tools.extend(server_tools)

        return tools

    @classmethod
    async def _list_tools_with_span(
        cls,
        server: MCPServer,
        run_context: RunContextWrapper[Any],
        agent: AgentBase,
    ) -> list[MCPTool]:
        with mcp_tools_span(server=server.name) as span:
            tools = await server.list_tools(run_context, agent)
            span.span_data.result = [tool.name for tool in tools]
            return tools

    @classmethod
    def _convert_mcp_tools_to_function_tools(
        cls,
        tools: list[MCPTool],
        server: MCPServer,
        convert_schemas_to_strict: bool,
        agent: AgentBase,
        failure_error_function: ToolErrorFunction | None = default_tool_error_function,
        tool_name_overrides: list[str] | None = None,
    ) -> list[Tool]:
        return [
            cls.to_function_tool(
                tool,
                server,
                convert_schemas_to_strict,
                agent,
                failure_error_function=failure_error_function,
                tool_name_override=(
                    tool_name_overrides[index] if tool_name_overrides is not None else None
                ),
            )
            for index, tool in enumerate(tools)
        ]

    @classmethod
    async def get_function_tools(
        cls,
        server: MCPServer,
        convert_schemas_to_strict: bool,
        run_context: RunContextWrapper[Any],
        agent: AgentBase,
        failure_error_function: ToolErrorFunction | None = default_tool_error_function,
        include_server_in_tool_names: bool = False,
        tool_name_override: Callable[[MCPTool], str] | None = None,
        reserved_tool_names: set[str] | None = None,
        server_index: int = 0,
    ) -> list[Tool]:
        """Get all function tools from a single MCP server."""

        tools = await cls._list_tools_with_span(server, run_context, agent)

        tool_name_overrides: list[str] | None = None
        if tool_name_override is not None:
            tool_name_overrides = [tool_name_override(tool) for tool in tools]
        elif include_server_in_tool_names:
            prefixed_tool_name_overrides = cls._build_prefixed_tool_name_overrides(
                [(server_index, server, tools)],
                reserved_names=set(reserved_tool_names or set()),
            )
            tool_name_overrides = [
                prefixed_tool_name_overrides[(server_index, tool_index)]
                for tool_index in range(len(tools))
            ]

        return cls._convert_mcp_tools_to_function_tools(
            tools,
            server,
            convert_schemas_to_strict,
            agent,
            failure_error_function=failure_error_function,
            tool_name_overrides=tool_name_overrides,
        )

    @staticmethod
    def _safe_tool_name_part(value: str, fallback: str) -> str:
        safe = "".join(
            char if char.isascii() and (char.isalnum() or char in {"_", "-"}) else "_"
            for char in value
        )
        safe = safe.strip("_-")
        return safe or fallback

    @staticmethod
    def _shorten_tool_name(base_name: str, seed: str, *, force_hash: bool = False) -> str:
        if not force_hash and len(base_name) <= _MCP_FUNCTION_TOOL_NAME_MAX_LENGTH:
            return base_name

        hash_suffix = hashlib.sha1(seed.encode("utf-8")).hexdigest()[
            :_MCP_FUNCTION_TOOL_HASH_LENGTH
        ]
        suffix = f"_{hash_suffix}"
        stem_length = _MCP_FUNCTION_TOOL_NAME_MAX_LENGTH - len(suffix)
        stem = base_name[:stem_length].rstrip("_-") or "mcp"
        return f"{stem}{suffix}"

    @classmethod
    def _build_prefixed_tool_base_name(cls, server_name: str, tool_name: str) -> str:
        server_part = cls._safe_tool_name_part(server_name, "server")
        tool_part = cls._safe_tool_name_part(tool_name, "tool")
        return f"mcp_{server_part}__{tool_part}"

    @classmethod
    def _build_prefixed_tool_name_overrides(
        cls,
        server_tool_batches: list[tuple[int, MCPServer, list[MCPTool]]],
        *,
        reserved_names: set[str],
    ) -> dict[tuple[int, int], str]:
        """Allocate public tool names for one in-memory MCP listing batch.

        Keys are batch-local `(server_index, tool_index)` coordinates, so this mapping does
        not depend on object identity or cross any serialization boundary.
        """
        base_names = [
            cls._build_prefixed_tool_base_name(server.name, tool.name)
            for _, server, tools in server_tool_batches
            for tool in tools
        ]
        base_name_counts = Counter(base_names)

        candidates: list[_PrefixedToolNameCandidate] = []
        for server_index, server, tools in server_tool_batches:
            for tool_index, tool in enumerate(tools):
                base_name = cls._build_prefixed_tool_base_name(server.name, tool.name)
                seed = f"{server.name}\0{tool.name}"
                force_hash = base_name_counts[base_name] > 1 or base_name in reserved_names
                initial_name = cls._shorten_tool_name(base_name, seed, force_hash=force_hash)
                candidates.append(
                    _PrefixedToolNameCandidate(
                        batch_key=(server_index, tool_index),
                        base_name=base_name,
                        seed=seed,
                        initial_name=initial_name,
                        server_index=server_index,
                        tool_index=tool_index,
                    )
                )

        used_names = set(reserved_names)
        tool_name_overrides: dict[tuple[int, int], str] = {}
        for candidate in sorted(
            candidates,
            key=lambda item: (
                item.initial_name,
                item.seed,
                item.server_index,
                item.tool_index,
            ),
        ):
            public_name = candidate.initial_name
            collision_index = 1
            while public_name in used_names:
                public_name = cls._shorten_tool_name(
                    candidate.base_name,
                    f"{candidate.seed}\0{collision_index}",
                    force_hash=True,
                )
                collision_index += 1

            used_names.add(public_name)
            tool_name_overrides[candidate.batch_key] = public_name

        return tool_name_overrides

    @classmethod
    def to_function_tool(
        cls,
        tool: MCPTool,
        server: MCPServer,
        convert_schemas_to_strict: bool,
        agent: AgentBase | None = None,
        failure_error_function: ToolErrorFunction | None = default_tool_error_function,
        tool_name_override: str | None = None,
    ) -> FunctionTool:
        """Convert an MCP tool to an Agents SDK function tool.

        The ``agent`` parameter is optional for backward compatibility with older
        call sites that used ``MCPUtil.to_function_tool(tool, server, strict)``.
        When omitted, this helper preserves the historical behavior for static
        policies. If the server uses a callable approval policy, approvals default
        to required to avoid bypassing dynamic checks.
        """
        tool_public_name = tool_name_override or tool.name
        static_meta = cls._extract_static_meta(tool)
        invoke_func_impl = functools.partial(
            cls.invoke_mcp_tool,
            server,
            tool,
            tool_display_name=tool_public_name,
            meta=static_meta,
        )
        effective_failure_error_function = server._get_failure_error_function(
            failure_error_function
        )
        schema, is_strict = copy.deepcopy(tool.inputSchema), False

        # MCP spec doesn't require the inputSchema to have `properties`, but OpenAI spec does.
        if "properties" not in schema:
            schema["properties"] = {}

        if convert_schemas_to_strict:
            # ``ensure_strict_json_schema`` mutates the schema in place and may raise
            # partway through, leaving strict-mode artifacts (e.g. ``required`` or
            # ``additionalProperties: false``) on a schema we still serve as
            # non-strict. Convert a separate copy so the non-strict fallback keeps
            # the original schema intact.
            try:
                schema = ensure_strict_json_schema(copy.deepcopy(schema))
                is_strict = True
            except Exception as e:
                logger.info("Error converting MCP schema to strict mode: %s", e)

        needs_approval: (
            bool | Callable[[RunContextWrapper[Any], dict[str, Any], str], Awaitable[bool]]
        ) = server._get_needs_approval_for_tool(tool, agent)

        function_tool = _build_wrapped_function_tool(
            name=tool_public_name,
            description=resolve_mcp_tool_description_for_model(tool),
            params_json_schema=schema,
            invoke_tool_impl=invoke_func_impl,
            on_handled_error=_build_handled_function_tool_error_handler(
                span_message="Error running tool (non-fatal)",
                log_label="MCP tool",
            ),
            failure_error_function=effective_failure_error_function,
            strict_json_schema=is_strict,
            needs_approval=needs_approval,
            mcp_title=resolve_mcp_tool_title(tool),
            tool_origin=ToolOrigin(
                type=ToolOriginType.MCP,
                mcp_server_name=server.name,
            ),
        )
        return function_tool

    @staticmethod
    def _merge_mcp_meta(
        resolved_meta: dict[str, Any] | None,
        explicit_meta: dict[str, Any] | None,
    ) -> dict[str, Any] | None:
        if resolved_meta is None and explicit_meta is None:
            return None
        merged: dict[str, Any] = {}
        if resolved_meta is not None:
            merged.update(copy.deepcopy(resolved_meta))
        if explicit_meta is not None:
            merged.update(copy.deepcopy(explicit_meta))
        return merged

    @staticmethod
    def _copy_mapping_proxy(value: Any) -> Mapping[str, Any] | None:
        if not isinstance(value, dict):
            return None
        return MappingProxyType(copy.deepcopy(value))

    @classmethod
    async def _extract_custom_data(
        cls,
        *,
        server: MCPServer,
        context: RunContextWrapper[Any],
        tool_name: str,
        tool_display_name: str,
        arguments: dict[str, Any],
        result: Any,
        tool_output: ToolOutput,
    ) -> dict[str, Any] | None:
        extractor = getattr(server, "custom_data_extractor", None)
        if extractor is None:
            return None

        extractor_context = MCPToolCustomDataContext(
            run_context=context,
            server_name=server.name,
            tool_name=tool_name,
            tool_display_name=tool_display_name,
            arguments=MappingProxyType(copy.deepcopy(arguments)),
            result_meta=cls._copy_mapping_proxy(getattr(result, "meta", None)),
            structured_content=cls._copy_mapping_proxy(getattr(result, "structuredContent", None)),
            is_error=getattr(result, "isError", None),
            tool_output=copy.deepcopy(tool_output),
        )
        return await maybe_extract_custom_data(extractor, extractor_context)

    @classmethod
    async def _resolve_meta(
        cls,
        server: MCPServer,
        context: RunContextWrapper[Any],
        tool_name: str,
        arguments: dict[str, Any] | None,
    ) -> dict[str, Any] | None:
        meta_resolver = getattr(server, "tool_meta_resolver", None)
        if meta_resolver is None:
            return None

        arguments_copy = copy.deepcopy(arguments) if arguments is not None else None
        resolver_context = MCPToolMetaContext(
            run_context=context,
            server_name=server.name,
            tool_name=tool_name,
            arguments=arguments_copy,
        )
        result = meta_resolver(resolver_context)
        if inspect.isawaitable(result):
            result = await result
        if result is None:
            return None
        if not isinstance(result, dict):
            raise TypeError("MCP meta resolver must return a dict or None.")
        return result

    @classmethod
    async def invoke_mcp_tool(
        cls,
        server: MCPServer,
        tool: MCPTool,
        context: RunContextWrapper[Any],
        input_json: str,
        *,
        meta: dict[str, Any] | None = None,
        tool_display_name: str | None = None,
    ) -> ToolOutput:
        """Invoke an MCP tool and return the result as ToolOutput."""
        tool_name_for_display = tool_display_name or tool.name
        json_decode_error: Exception | None = None
        try:
            json_data = json.loads(input_json) if input_json else {}
        except Exception as e:
            json_decode_error = e

        if json_decode_error is not None:
            error_message = f"Invalid JSON input for tool {tool_name_for_display}"
            if _debug.DONT_LOG_TOOL_DATA:
                logger.debug(error_message)
                raise ModelBehaviorError(error_message)
            else:
                error_message = f"{error_message}: {input_json}"
                logger.debug(error_message)
            raise ModelBehaviorError(error_message) from json_decode_error

        if not isinstance(json_data, dict):
            raise ModelBehaviorError(
                f"Invalid JSON input for tool {tool_name_for_display}: expected a JSON object"
            )

        if _debug.DONT_LOG_TOOL_DATA:
            logger.debug("Invoking MCP tool %s", tool_name_for_display)
        else:
            logger.debug("Invoking MCP tool %s with input %s", tool_name_for_display, input_json)

        try:
            resolved_meta = await cls._resolve_meta(server, context, tool.name, json_data)
            merged_meta = cls._merge_mcp_meta(resolved_meta, meta)
            call_task = asyncio.create_task(
                server.call_tool(tool.name, json_data)
                if merged_meta is None
                else server.call_tool(tool.name, json_data, meta=merged_meta)
            )
            try:
                done, _ = await asyncio.wait({call_task}, return_when=asyncio.FIRST_COMPLETED)
                finished_task = done.pop()
                if finished_task.cancelled():
                    raise MCPToolCancellationError(
                        f"Failed to call tool '{tool.name}' on MCP server '{server.name}': "
                        "tool execution was cancelled."
                    )
                result = finished_task.result()
            except asyncio.CancelledError:
                if not call_task.done():
                    call_task.cancel()
                try:
                    await call_task
                except (asyncio.CancelledError, Exception):
                    pass
                raise
        except (UserError, MCPToolCancellationError):
            # Re-raise handled tool-call errors as-is; the FunctionTool failure pipeline
            # will format them into model-visible tool errors when appropriate.
            raise
        except Exception as e:
            if _McpError is not None and isinstance(e, _McpError):
                # An MCP-level error (e.g. upstream HTTP 4xx/5xx, tool not found, etc.)
                # is not a programming error – re-raise so the FunctionTool failure
                # pipeline (failure_error_function) can handle it.  The default handler
                # will surface the message as a structured error result; callers who set
                # failure_error_function=None will have the error raised as documented.
                error_text = e.error.message if hasattr(e, "error") and e.error else str(e)
                logger.warning(
                    "MCP tool %s on server '%s' returned an error: %s",
                    tool_name_for_display,
                    server.name,
                    error_text,
                )
                raise

            logger.error(
                "Error invoking MCP tool %s on server '%s': %s",
                tool_name_for_display,
                server.name,
                e,
            )
            raise AgentsException(
                f"Error invoking MCP tool {tool_name_for_display} on server '{server.name}': {e}"
            ) from e

        if _debug.DONT_LOG_TOOL_DATA:
            logger.debug("MCP tool %s completed.", tool_name_for_display)
        else:
            logger.debug("MCP tool %s returned %s", tool_name_for_display, result)

        # If structured content is requested and available, use it exclusively
        tool_output: ToolOutput
        if server.use_structured_content and result.structuredContent:
            tool_output = json.dumps(result.structuredContent)
        else:
            tool_output_list: list[ToolOutputItem] = []
            for item in result.content:
                if item.type == "text":
                    tool_output_list.append(ToolOutputTextDict(type="text", text=item.text))
                elif item.type == "image":
                    tool_output_list.append(
                        ToolOutputImageDict(
                            type="image", image_url=f"data:{item.mimeType};base64,{item.data}"
                        )
                    )
                else:
                    # Fall back to regular text content
                    tool_output_list.append(
                        ToolOutputTextDict(type="text", text=str(item.model_dump(mode="json")))
                    )
            if len(tool_output_list) == 1:
                tool_output = tool_output_list[0]
            else:
                tool_output = tool_output_list

        custom_data = await cls._extract_custom_data(
            server=server,
            context=context,
            tool_name=tool.name,
            tool_display_name=tool_name_for_display,
            arguments=json_data,
            result=result,
            tool_output=tool_output,
        )
        if custom_data and isinstance(context, ToolContext):
            context._custom_data = custom_data

        current_span = get_current_span()
        if current_span:
            if isinstance(current_span.span_data, FunctionSpanData):
                if not isinstance(context, ToolContext) or (
                    context.run_config is None or context.run_config.trace_include_sensitive_data
                ):
                    current_span.span_data.output = tool_output
                current_span.span_data.mcp_data = {
                    "server": server.name,
                }
            else:
                logger.warning(
                    "Current span is not a FunctionSpanData, skipping tool output: %s", current_span
                )

        return tool_output
