fix: Add response schema for agent tool function declaration even when it's return None

PiperOrigin-RevId: 783610166

Author: seanzhougoogle

src/google/adk/tools/_automatic_function_calling_util.py

@@ -20,7 +20,6 @@
 from typing import Any
 from typing import Callable
 from typing import Dict
-from typing import Literal
 from typing import Optional
 from typing import Union
 
@@ -329,7 +328,26 @@ def from_function_with_options(
     return declaration
 
   return_annotation = inspect.signature(func).return_annotation
-  if return_annotation is inspect._empty:
+
+  # Handle functions with no return annotation or that return None
+  if (
+      return_annotation is inspect._empty
+      or return_annotation is None
+      or return_annotation is type(None)
+  ):
+    # Create a response schema for None/null return
+    return_value = inspect.Parameter(
+        'return_value',
+        inspect.Parameter.POSITIONAL_OR_KEYWORD,
+        annotation=None,
+    )
+    declaration.response = (
+        _function_parameter_parse_util._parse_schema_from_parameter(
+            variant,
+            return_value,
+            func.__name__,
+        )
+    )
     return declaration
 
   return_value = inspect.Parameter(

src/google/adk/tools/agent_tool.py

@@ -61,6 +61,7 @@ def populate_name(cls, data: Any) -> Any:
   @override
   def _get_declaration(self) -> types.FunctionDeclaration:
     from ..agents.llm_agent import LlmAgent
+    from ..utils.variant_utils import GoogleLLMVariant
 
     if isinstance(self.agent, LlmAgent) and self.agent.input_schema:
       result = _automatic_function_calling_util.build_function_declaration(
@@ -80,6 +81,17 @@ def _get_declaration(self) -> types.FunctionDeclaration:
           description=self.agent.description,
           name=self.name,
       )
+
+    # Set response schema for non-GEMINI_API variants
+    if self._api_variant != GoogleLLMVariant.GEMINI_API:
+      # Determine response type based on agent's output schema
+      if isinstance(self.agent, LlmAgent) and self.agent.output_schema:
+        # Agent has structured output schema - response is an object
+        result.response = types.Schema(type=types.Type.OBJECT)
+      else:
+        # Agent returns text - response is a string
+        result.response = types.Schema(type=types.Type.STRING)
+
     result.name = self.name
     return result
 

tests/unittests/tools/test_agent_tool.py

@@ -16,6 +16,8 @@
 from google.adk.agents import SequentialAgent
 from google.adk.agents.callback_context import CallbackContext
 from google.adk.tools.agent_tool import AgentTool
+from google.adk.utils.variant_utils import GoogleLLMVariant
+from google.genai import types
 from google.genai.types import Part
 from pydantic import BaseModel
 from pytest import mark
@@ -209,3 +211,147 @@ class CustomOutput(BaseModel):
   # The second request is the tool agent request.
   assert mock_model.requests[1].config.response_schema == CustomOutput
   assert mock_model.requests[1].config.response_mime_type == 'application/json'
+
+
+@mark.parametrize(
+    'env_variables',
+    [
+        'VERTEX',  # Test VERTEX_AI variant
+    ],
+    indirect=True,
+)
+def test_agent_tool_response_schema_no_output_schema_vertex_ai():
+  """Test AgentTool with no output schema has string response schema for VERTEX_AI."""
+  tool_agent = Agent(
+      name='tool_agent',
+      model=testing_utils.MockModel.create(responses=['test response']),
+  )
+
+  agent_tool = AgentTool(agent=tool_agent)
+  declaration = agent_tool._get_declaration()
+
+  assert declaration.name == 'tool_agent'
+  assert declaration.parameters.type == 'OBJECT'
+  assert declaration.parameters.properties['request'].type == 'STRING'
+  # Should have string response schema for VERTEX_AI
+  assert declaration.response is not None
+  assert declaration.response.type == types.Type.STRING
+
+
+@mark.parametrize(
+    'env_variables',
+    [
+        'VERTEX',  # Test VERTEX_AI variant
+    ],
+    indirect=True,
+)
+def test_agent_tool_response_schema_with_output_schema_vertex_ai():
+  """Test AgentTool with output schema has object response schema for VERTEX_AI."""
+
+  class CustomOutput(BaseModel):
+    custom_output: str
+
+  tool_agent = Agent(
+      name='tool_agent',
+      model=testing_utils.MockModel.create(responses=['test response']),
+      output_schema=CustomOutput,
+  )
+
+  agent_tool = AgentTool(agent=tool_agent)
+  declaration = agent_tool._get_declaration()
+
+  assert declaration.name == 'tool_agent'
+  # Should have object response schema for VERTEX_AI when output_schema exists
+  assert declaration.response is not None
+  assert declaration.response.type == types.Type.OBJECT
+
+
+@mark.parametrize(
+    'env_variables',
+    [
+        'GOOGLE_AI',  # Test GEMINI_API variant
+    ],
+    indirect=True,
+)
+def test_agent_tool_response_schema_gemini_api():
+  """Test AgentTool with GEMINI_API variant has no response schema."""
+
+  class CustomOutput(BaseModel):
+    custom_output: str
+
+  tool_agent = Agent(
+      name='tool_agent',
+      model=testing_utils.MockModel.create(responses=['test response']),
+      output_schema=CustomOutput,
+  )
+
+  agent_tool = AgentTool(agent=tool_agent)
+  declaration = agent_tool._get_declaration()
+
+  assert declaration.name == 'tool_agent'
+  # GEMINI_API should not have response schema
+  assert declaration.response is None
+
+
+@mark.parametrize(
+    'env_variables',
+    [
+        'VERTEX',  # Test VERTEX_AI variant
+    ],
+    indirect=True,
+)
+def test_agent_tool_response_schema_with_input_schema_vertex_ai():
+  """Test AgentTool with input and output schemas for VERTEX_AI."""
+
+  class CustomInput(BaseModel):
+    custom_input: str
+
+  class CustomOutput(BaseModel):
+    custom_output: str
+
+  tool_agent = Agent(
+      name='tool_agent',
+      model=testing_utils.MockModel.create(responses=['test response']),
+      input_schema=CustomInput,
+      output_schema=CustomOutput,
+  )
+
+  agent_tool = AgentTool(agent=tool_agent)
+  declaration = agent_tool._get_declaration()
+
+  assert declaration.name == 'tool_agent'
+  assert declaration.parameters.type == 'OBJECT'
+  assert declaration.parameters.properties['custom_input'].type == 'STRING'
+  # Should have object response schema for VERTEX_AI when output_schema exists
+  assert declaration.response is not None
+  assert declaration.response.type == types.Type.OBJECT
+
+
+@mark.parametrize(
+    'env_variables',
+    [
+        'VERTEX',  # Test VERTEX_AI variant
+    ],
+    indirect=True,
+)
+def test_agent_tool_response_schema_with_input_schema_no_output_vertex_ai():
+  """Test AgentTool with input schema but no output schema for VERTEX_AI."""
+
+  class CustomInput(BaseModel):
+    custom_input: str
+
+  tool_agent = Agent(
+      name='tool_agent',
+      model=testing_utils.MockModel.create(responses=['test response']),
+      input_schema=CustomInput,
+  )
+
+  agent_tool = AgentTool(agent=tool_agent)
+  declaration = agent_tool._get_declaration()
+
+  assert declaration.name == 'tool_agent'
+  assert declaration.parameters.type == 'OBJECT'
+  assert declaration.parameters.properties['custom_input'].type == 'STRING'
+  # Should have string response schema for VERTEX_AI when no output_schema
+  assert declaration.response is not None
+  assert declaration.response.type == types.Type.STRING

tests/unittests/tools/test_build_function_declaration.py

@@ -17,6 +17,8 @@
 
 from google.adk.tools import _automatic_function_calling_util
 from google.adk.tools.agent_tool import ToolContext
+from google.adk.utils.variant_utils import GoogleLLMVariant
+from google.genai import types
 # TODO: crewai requires python 3.10 as minimum
 # from crewai_tools import FileReadTool
 from pydantic import BaseModel
@@ -262,3 +264,118 @@ def simple_function(input_str: List[CustomInput]) -> str:
 #   assert function_decl.name == 'directory_read_tool'
 #   assert function_decl.parameters.type == 'OBJECT'
 #   assert function_decl.parameters.properties['file_path'].type == 'STRING'
+
+
+def test_function_no_return_annotation_gemini_api():
+  """Test function with no return annotation using GEMINI_API variant."""
+
+  def function_no_return(param: str):
+    """A function with no return annotation."""
+    return None
+
+  function_decl = _automatic_function_calling_util.build_function_declaration(
+      func=function_no_return, variant=GoogleLLMVariant.GEMINI_API
+  )
+
+  assert function_decl.name == 'function_no_return'
+  assert function_decl.parameters.type == 'OBJECT'
+  assert function_decl.parameters.properties['param'].type == 'STRING'
+  # GEMINI_API should not have response schema
+  assert function_decl.response is None
+
+
+def test_function_no_return_annotation_vertex_ai():
+  """Test function with no return annotation using VERTEX_AI variant."""
+
+  def function_no_return(param: str):
+    """A function with no return annotation."""
+    return None
+
+  function_decl = _automatic_function_calling_util.build_function_declaration(
+      func=function_no_return, variant=GoogleLLMVariant.VERTEX_AI
+  )
+
+  assert function_decl.name == 'function_no_return'
+  assert function_decl.parameters.type == 'OBJECT'
+  assert function_decl.parameters.properties['param'].type == 'STRING'
+  # VERTEX_AI should have response schema for None return
+  assert function_decl.response is not None
+  assert function_decl.response.type == types.Type.NULL
+
+
+def test_function_explicit_none_return_vertex_ai():
+  """Test function with explicit None return annotation using VERTEX_AI variant."""
+
+  def function_none_return(param: str) -> None:
+    """A function that explicitly returns None."""
+    pass
+
+  function_decl = _automatic_function_calling_util.build_function_declaration(
+      func=function_none_return, variant=GoogleLLMVariant.VERTEX_AI
+  )
+
+  assert function_decl.name == 'function_none_return'
+  assert function_decl.parameters.type == 'OBJECT'
+  assert function_decl.parameters.properties['param'].type == 'STRING'
+  # VERTEX_AI should have response schema for explicit None return
+  assert function_decl.response is not None
+  assert function_decl.response.type == types.Type.NULL
+
+
+def test_function_explicit_none_return_gemini_api():
+  """Test function with explicit None return annotation using GEMINI_API variant."""
+
+  def function_none_return(param: str) -> None:
+    """A function that explicitly returns None."""
+    pass
+
+  function_decl = _automatic_function_calling_util.build_function_declaration(
+      func=function_none_return, variant=GoogleLLMVariant.GEMINI_API
+  )
+
+  assert function_decl.name == 'function_none_return'
+  assert function_decl.parameters.type == 'OBJECT'
+  assert function_decl.parameters.properties['param'].type == 'STRING'
+  # GEMINI_API should not have response schema
+  assert function_decl.response is None
+
+
+def test_function_regular_return_type_vertex_ai():
+  """Test function with regular return type using VERTEX_AI variant."""
+
+  def function_string_return(param: str) -> str:
+    """A function that returns a string."""
+    return param
+
+  function_decl = _automatic_function_calling_util.build_function_declaration(
+      func=function_string_return, variant=GoogleLLMVariant.VERTEX_AI
+  )
+
+  assert function_decl.name == 'function_string_return'
+  assert function_decl.parameters.type == 'OBJECT'
+  assert function_decl.parameters.properties['param'].type == 'STRING'
+  # VERTEX_AI should have response schema for string return
+  assert function_decl.response is not None
+  assert function_decl.response.type == types.Type.STRING
+
+
+def test_transfer_to_agent_like_function():
+  """Test a function similar to transfer_to_agent that caused the original issue."""
+
+  def transfer_to_agent(agent_name: str, tool_context: ToolContext):
+    """Transfer the question to another agent."""
+    tool_context.actions.transfer_to_agent = agent_name
+
+  function_decl = _automatic_function_calling_util.build_function_declaration(
+      func=transfer_to_agent,
+      ignore_params=['tool_context'],
+      variant=GoogleLLMVariant.VERTEX_AI,
+  )
+
+  assert function_decl.name == 'transfer_to_agent'
+  assert function_decl.parameters.type == 'OBJECT'
+  assert function_decl.parameters.properties['agent_name'].type == 'STRING'
+  assert 'tool_context' not in function_decl.parameters.properties
+  # This should now have a response schema for VERTEX_AI variant
+  assert function_decl.response is not None
+  assert function_decl.response.type == types.Type.NULL