core[patch]: fix regression in convert_to_openai_tool with instances of Tool (#26327)

```python
from langchain_core.tools import Tool
from langchain_core.utils.function_calling import convert_to_openai_tool

def my_function(x: int) -> int:
    return x + 2

tool = Tool(
    name="tool_name",
    func=my_function,
    description="test description",
)
convert_to_openai_tool(tool)
```

Current:
```
{'type': 'function',
 'function': {'name': 'tool_name',
  'description': 'test description',
  'parameters': {'type': 'object',
   'properties': {'args': {'type': 'array', 'items': {}},
    'config': {'type': 'object',
     'properties': {'tags': {'type': 'array', 'items': {'type': 'string'}},
      'metadata': {'type': 'object'},
      'callbacks': {'anyOf': [{'type': 'array', 'items': {}}, {}]},
      'run_name': {'type': 'string'},
      'max_concurrency': {'type': 'integer'},
      'recursion_limit': {'type': 'integer'},
      'configurable': {'type': 'object'},
      'run_id': {'type': 'string', 'format': 'uuid'}}},
    'kwargs': {'type': 'object'}},
   'required': ['config']}}}
```

Here:
```
{'type': 'function',
 'function': {'name': 'tool_name',
  'description': 'test description',
  'parameters': {'properties': {'__arg1': {'title': '__arg1',
     'type': 'string'}},
   'required': ['__arg1'],
   'type': 'object'}}}
```

libs/core/langchain_core/utils/function_calling.py

@@ -280,7 +280,10 @@ def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
     Returns:
         The function description.
     """
-    if tool.tool_call_schema:
+    from langchain_core.tools import simple
+
+    is_simple_oai_tool = isinstance(tool, simple.Tool) and not tool.args_schema
+    if tool.tool_call_schema and not is_simple_oai_tool:
         return convert_pydantic_to_openai_function(
             tool.tool_call_schema, name=tool.name, description=tool.description
         )

libs/core/tests/unit_tests/utils/test_function_calling.py

@@ -37,7 +37,7 @@ except ImportError:
 from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.runnables import Runnable, RunnableLambda
-from langchain_core.tools import BaseTool, tool
+from langchain_core.tools import BaseTool, StructuredTool, Tool, tool
 from langchain_core.utils.function_calling import (
     _convert_typed_dict_to_openai_function,
     convert_to_openai_function,
@@ -111,6 +111,20 @@ def dummy_tool() -> BaseTool:
     return DummyFunction()
 
 
+@pytest.fixture()
+def dummy_structured_tool() -> StructuredTool:
+    class Schema(BaseModel):
+        arg1: int = Field(..., description="foo")
+        arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'")
+
+    return StructuredTool.from_function(
+        lambda x: None,
+        name="dummy_function",
+        description="dummy function",
+        args_schema=Schema,
+    )
+
+
 @pytest.fixture()
 def dummy_pydantic() -> Type[BaseModel]:
     class dummy_function(BaseModel):
@@ -233,6 +247,7 @@ class DummyWithClassMethod:
 def test_convert_to_openai_function(
     pydantic: Type[BaseModel],
     function: Callable,
+    dummy_structured_tool: StructuredTool,
     dummy_tool: BaseTool,
     json_schema: Dict,
     Annotated_function: Callable,
@@ -263,6 +278,7 @@ def test_convert_to_openai_function(
     for fn in (
         pydantic,
         function,
+        dummy_structured_tool,
         dummy_tool,
         json_schema,
         expected,
@@ -295,6 +311,27 @@ def test_convert_to_openai_function(
     runnable_expected["parameters"] = parameters
     assert actual == runnable_expected
 
+    # Test simple Tool
+    def my_function(input_string: str) -> str:
+        pass
+
+    tool = Tool(
+        name="dummy_function",
+        func=my_function,
+        description="test description",
+    )
+    actual = convert_to_openai_function(tool)
+    expected = {
+        "name": "dummy_function",
+        "description": "test description",
+        "parameters": {
+            "properties": {"__arg1": {"title": "__arg1", "type": "string"}},
+            "required": ["__arg1"],
+            "type": "object",
+        },
+    }
+    assert actual == expected
+
 
 @pytest.mark.xfail(reason="Direct pydantic v2 models not yet supported")
 def test_convert_to_openai_function_nested_v2() -> None: