[BUG]: dd-trace-py is unable to capture traces/spans when using Google ADK function tools

[NclsL]

Tracer Version(s)

3.18.0

Python Version(s)

3.13.8

Pip Version(s)

21.2.4

Bug Report

See reproduction code, we're using function tool and pass it to the Agent as tool.

I assume ddtrace is expecting these 2 attributes name and description, but as they're not available it leads to errors.

Reproduction Code

import os
from typing import Any
from ddtrace.llmobs import LLMObs
from google.adk.agents import Agent
from google.adk.runners import Runner, types
from google.adk.sessions import InMemorySessionService


def get_weather(city: str) -> dict[str, Any]:
    """Retrieves the current weather report for a specified city.

    Args:
        city (str): The name of the city for which to retrieve the weather report.

    Returns:
        dict: status and result or error msg.
    """
    if city.lower() == "new york":
        return {
            "status": "success",
            "report": (
                "The weather in New York is sunny with a temperature of 25 degrees"
                " Celsius (77 degrees Fahrenheit)."
            ),
        }
    else:
        return {
            "status": "error",
            "error_message": f"Weather information for '{city}' is not available.",
        }


async def main() -> None:
    LLMObs.enable(
        ml_app="app_name",
        api_key="fill_in",
        site="fill_in",
        agentless_enabled=True    
    )

    os.environ["GOOGLE_CLOUD_PROJECT"] = "fill_in"
    os.environ["GOOGLE_CLOUD_LOCATION"] = "fill_in"
    os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "true"

    agent = Agent(
        name="weather_agent",
        model="gemini-2.0-flash",
        description="Agent to answer questions about the weather in a city.",
        instruction=(
            "Always provide the weather details in a friendly and informative manner"
        ),
        tools=[get_weather],
    )

    session_service = InMemorySessionService()
    runner = Runner(
        app_name="app_name",
        agent=agent,
        session_service=session_service,
    )

    session = await session_service.create_session(app_name="app_name", user_id="test_user")

    message = types.Content(
        parts=[types.Part(text="What's the weather like in New York?")], role="user")

    events = [
        event
        async for event in runner.run_async(
            session_id=session.id,
            user_id="test_user",
            new_message=message,
        )
    ]

    response = "No response"
    if events:
        for event in reversed(events):
            content = getattr(event, "content", None)
            if content and hasattr(content, "parts"):
                text_parts = [
                    p.text for p in content.parts if hasattr(p, "text")]
                if text_parts:
                    response = "".join(text_parts)
                    break

    print(f"\nResponse: {response}\n")


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())

Error Logs

Error extracting LLMObs fields for span <Span(id=redacted,trace_id=redacted,parent_id=None,name=google_adk.request)>, likely due to malformed data
Traceback (most recent call last):
  File "/app/.venv/lib/python3.13/site-packages/ddtrace/llmobs/_integrations/base.py", line 105, in llmobs_set_tags
    self._llmobs_set_tags(span, args, kwargs, response, operation)
    ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/.venv/lib/python3.13/site-packages/ddtrace/llmobs/_integrations/google_adk.py", line 46, in _llmobs_set_tags
    self._llmobs_set_tags_agent(span, args, kwargs, response)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/.venv/lib/python3.13/site-packages/ddtrace/llmobs/_integrations/google_adk.py", line 66, in _llmobs_set_tags_agent
    self._tag_agent_manifest(span, kwargs, agent_instance)
    ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/.venv/lib/python3.13/site-packages/ddtrace/llmobs/_integrations/google_adk.py", line 130, in _tag_agent_manifest
    manifest["tools"] = self._get_agent_tools(getattr(agent, "tools", []))
                        ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/.venv/lib/python3.13/site-packages/ddtrace/llmobs/_integrations/google_adk.py", line 157, in _get_agent_tools
    return [{"name": tool.name, "description": tool.description} for tool in tools]
                     ^^^^^^^^^
AttributeError: 'function' object has no attribute 'name'

Libraries in Use

ddtrace>=3.18.0,
google-adk==1.18.0

Operating System

MacOS Tahoe 26.0.1

[NclsL]

For those who stumble across this issue, we also spent some time on workaround, but it is quite verbose. The important part is creating a tool class that inherits the BaseTool class. With this we were able to submit traces and spans to LLMObs

import os
from typing import Any
from typing_extensions import override


from ddtrace.llmobs import LLMObs
from google.adk.agents import Agent
from google.adk.runners import Runner, types
from google.adk.sessions import InMemorySessionService
from google.adk.tools import google_search, BaseTool, ToolContext
from google.genai.types import FunctionDeclaration


class WeatherTool(BaseTool):
    def __init__(self):
        """Initialize the WeatherTool with name and description. We need them for datadog LLM obs"""
        super().__init__(
            name="get_weather",
            description="Retrieves the current weather report for a specified city."
        )

    @override
    def _get_declaration(self) -> FunctionDeclaration:
        """Returns the function declaration for this tool.

        This defines the interface that the LLM will use to call the tool,
        including parameters and their descriptions.
        """
        return FunctionDeclaration(
            name=self.name,
            description=self.description,
            parameters={
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "The name of the city for which to retrieve the weather report."
                    }
                },
                "required": ["city"]
            }
        )

    @override
    async def run_async(self, args: dict[str, Any], tool_context: ToolContext) -> dict[str, Any]:
        """Execute the weather tool to fetch weather information.

        Args:
            args: A dictionary containing the 'city' parameter.
            tool_context: The tool execution context provided by ADK.

        Returns:
            dict: status and result or error msg.
        """
        city = args.get("city", "")

        if not city:
            return {
                "status": "error",
                "error_message": "City parameter is required.",
            }

        if city.lower() == "new york":
            return {
                "status": "success",
                "city": city,
                "temperature_celsius": 25,
                "temperature_fahrenheit": 77,
                "condition": "sunny",
                "report": (
                    "The weather in New York is sunny with a temperature of 25 degrees"
                    " Celsius (77 degrees Fahrenheit)."
                ),
            }
        else:
            return {
                "status": "error",
                "error_message": f"Weather information for '{city}' is not available.",
            }

async def main() -> None:
    LLMObs.enable(...)
    os.environ["GOOGLE_CLOUD_PROJECT"] = "..."

    weather_tool = WeatherTool()
    agent = Agent(
        name="weather_agent",
        model="gemini-2.0-flash",
        description="Agent to answer questions about the weather in a city.",
        instruction=(
            "Always provide the weather details in a friendly and informative manner"
        ),
        tools=[weather_tool],
    )
    # The rest is the same