diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py
index 394bf5a2..84f6bb38 100644
--- a/libs/vertexai/langchain_google_vertexai/chat_models.py
+++ b/libs/vertexai/langchain_google_vertexai/chat_models.py
@@ -58,7 +58,7 @@
 from langchain_core.output_parsers.openai_tools import parse_tool_calls
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.pydantic_v1 import BaseModel, root_validator, Field
-from langchain_core.runnables import Runnable, RunnablePassthrough
+from langchain_core.runnables import Runnable, RunnablePassthrough, RunnableGenerator
 from vertexai.generative_models import (  # type: ignore
     Tool as VertexTool,
 )
@@ -1497,6 +1497,15 @@ def with_structured_output(
     ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
         """Model wrapper that returns outputs formatted to match the given schema.
 
+        .. versionchanged:: 1.1.0
+
+            Return type corrected in version 1.1.0. Previously if a dict schema
+            was provided then the output had the form
+            ``[{"args": {}, "name": "schema_name"}]`` where the output was a list with
+            a single dict and the "args" of the one dict corresponded to the schema.
+            As of `1.1.0` this has been fixed so that the schema (the value
+            corresponding to the old "args" key) is returned directly.
+
         Args:
             schema: The output schema as a dict or a Pydantic class. If a Pydantic class
                 then the model output will be an object of that class. If a dict then
@@ -1589,7 +1598,9 @@ class AnswerWithJustification(BaseModel):
                 tools=[schema], first_tool_only=True
             )
         else:
-            parser = JsonOutputToolsParser()
+            parser = JsonOutputToolsParser(first_tool_only=True) | RunnableGenerator(
+                _yield_args
+            )
         llm = self.bind_tools([schema], tool_choice=self._is_gemini_advanced)
         if include_raw:
             parser_with_fallback = RunnablePassthrough.assign(
@@ -1707,6 +1718,11 @@ def _gemini_chunk_to_generation_chunk(
         )
 
 
+def _yield_args(tool_call_chunks: Iterator[dict]) -> Iterator[dict]:
+    for tc in tool_call_chunks:
+        yield tc["args"]
+
+
 def _get_usage_metadata_gemini(raw_metadata: dict) -> Optional[UsageMetadata]:
     """Get UsageMetadata from raw response metadata."""
     input_tokens = raw_metadata.get("prompt_token_count", 0)
diff --git a/libs/vertexai/langchain_google_vertexai/functions_utils.py b/libs/vertexai/langchain_google_vertexai/functions_utils.py
index 771fd70f..10cc7676 100644
--- a/libs/vertexai/langchain_google_vertexai/functions_utils.py
+++ b/libs/vertexai/langchain_google_vertexai/functions_utils.py
@@ -24,7 +24,10 @@
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.tools import BaseTool
 from langchain_core.tools import tool as callable_as_lc_tool
-from langchain_core.utils.function_calling import FunctionDescription
+from langchain_core.utils.function_calling import (
+    FunctionDescription,
+    convert_to_openai_tool,
+)
 from langchain_core.utils.json_schema import dereference_refs
 
 logger = logging.getLogger(__name__)
@@ -169,11 +172,8 @@ def _format_to_gapic_function_declaration(
     elif isinstance(tool, dict):
         # this could come from
         # 'langchain_core.utils.function_calling.convert_to_openai_tool'
-        if tool.get("type") == "function" and tool.get("function"):
-            return _format_dict_to_function_declaration(
-                cast(FunctionDescription, tool.get("function"))
-            )
-        return _format_dict_to_function_declaration(tool)
+        function = convert_to_openai_tool(cast(dict, tool))["function"]
+        return _format_dict_to_function_declaration(cast(FunctionDescription, function))
     else:
         raise ValueError(f"Unsupported tool call type {tool}")
 
diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py
index a50f8c90..0bfe2a9b 100644
--- a/libs/vertexai/tests/integration_tests/test_chat_models.py
+++ b/libs/vertexai/tests/integration_tests/test_chat_models.py
@@ -545,16 +545,28 @@ class MyModel(BaseModel):
         {"name": "MyModel", "description": "MyModel", "parameters": MyModel.schema()}
     )
     response = model.invoke([message])
-    expected = [
+    assert response == {
+        "name": "Erick",
+        "age": 27,
+    }
+
+    model = llm.with_structured_output(
         {
-            "type": "MyModel",
-            "args": {
-                "name": "Erick",
-                "age": 27,
+            "title": "MyModel",
+            "description": "MyModel",
+            "type": "object",
+            "properties": {
+                "name": {"type": "string"},
+                "age": {"type": "integer"},
             },
+            "required": ["name", "age"],
         }
-    ]
-    assert response == expected
+    )
+    response = model.invoke([message])
+    assert response == {
+        "name": "Erick",
+        "age": 27,
+    }
 
 
 @pytest.mark.release