diff --git a/python/packages/autogen-core/docs/src/user-guide/core-user-guide/framework/tools.ipynb b/python/packages/autogen-core/docs/src/user-guide/core-user-guide/framework/tools.ipynb
index 0214b0286..183d878e4 100644
--- a/python/packages/autogen-core/docs/src/user-guide/core-user-guide/framework/tools.ipynb
+++ b/python/packages/autogen-core/docs/src/user-guide/core-user-guide/framework/tools.ipynb
@@ -59,6 +59,13 @@
     "print(code_execution_tool.return_value_as_string(result))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -82,6 +89,11 @@
     "To create a custom function tool, you just need to create a Python function\n",
     "and use the {py:class}`~autogen_core.components.tools.FunctionTool` class to wrap it.\n",
     "\n",
+    "The {py:class}`~autogen_core.components.tools.FunctionTool` class uses descriptions and type annotations\n",
+    "to inform the LLM when and how to use a given function. The description provides context\n",
+    "about the function’s purpose and intended use cases, while type annotations inform the LLM about\n",
+    "the expected parameters and return type.\n",
+    "\n",
     "For example, a simple tool to obtain the stock price of a company might look like this:"
    ]
   },
@@ -296,7 +308,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/python/packages/autogen-core/src/autogen_core/components/tools/_function_tool.py b/python/packages/autogen-core/src/autogen_core/components/tools/_function_tool.py
index e537deeba..462374116 100644
--- a/python/packages/autogen-core/src/autogen_core/components/tools/_function_tool.py
+++ b/python/packages/autogen-core/src/autogen_core/components/tools/_function_tool.py
@@ -13,6 +13,52 @@ from ._base import BaseTool
 
 
 class FunctionTool(BaseTool[BaseModel, BaseModel]):
+    """
+    Create custom tools by wrapping standard Python functions.
+
+    `FunctionTool` offers an interface for executing Python functions either asynchronously or synchronously.
+    Each function must include type annotations for all parameters and its return type. These annotations
+    enable `FunctionTool` to generate a schema necessary for input validation, serialization, and for informing
+    the LLM about expected parameters. When the LLM prepares a function call, it leverages this schema to
+    generate arguments that align with the function's specifications.
+
+    .. note::
+
+        It is the user's responsibility to verify that the tool's output type matches the expected type.
+
+    Args:
+        func (Callable[..., ReturnT | Awaitable[ReturnT]]): The function to wrap and expose as a tool.
+        description (str): A description to inform the model of the function's purpose, specifying what
+            it does and the context in which it should be called.
+        name (str, optional): An optional custom name for the tool. Defaults to
+            the function's original name if not provided.
+
+    Example:
+
+        .. code-block:: python
+
+            import random
+            from autogen_core.base import CancellationToken
+            from autogen_core.components.tools import FunctionTool
+            from typing_extensions import Annotated
+
+
+            async def get_stock_price(ticker: str, date: Annotated[str, "Date in YYYY/MM/DD"]) -> float:
+                # Simulates a stock price retrieval by returning a random float within a specified range.
+                return random.uniform(10, 200)
+
+
+            # Initialize a FunctionTool instance for retrieving stock prices.
+            stock_price_tool = FunctionTool(get_stock_price, description="Fetch the stock price for a given ticker.")
+
+            # Execute the tool with cancellation support.
+            cancellation_token = CancellationToken()
+            result = await stock_price_tool.run_json({"ticker": "AAPL", "date": "2021/01/01"}, cancellation_token)
+
+            # Output the result as a formatted string.
+            print(stock_price_tool.return_value_as_string(result))
+    """
+
     def __init__(self, func: Callable[..., Any], description: str, name: str | None = None) -> None:
         self._func = func
         signature = get_typed_signature(func)
@@ -46,6 +92,4 @@ class FunctionTool(BaseTool[BaseModel, BaseModel]):
                 cancellation_token.link_future(future)
                 result = await future
 
-        if not isinstance(result, self.return_type()):
-            raise ValueError(f"Expected return type {self.return_type()}, got {type(result)}")
         return result
diff --git a/python/packages/autogen-core/tests/test_tools.py b/python/packages/autogen-core/tests/test_tools.py
index 2a0c82b8f..f41b5edd4 100644
--- a/python/packages/autogen-core/tests/test_tools.py
+++ b/python/packages/autogen-core/tests/test_tools.py
@@ -1,5 +1,5 @@
 import inspect
-from typing import Annotated
+from typing import Annotated, List
 
 import pytest
 from autogen_core.base import CancellationToken
@@ -324,3 +324,15 @@ def test_convert_tools_accepts_both_tool_and_schema() -> None:
 
     assert len(converted_tool_schema) == 2
     assert converted_tool_schema[0] == converted_tool_schema[1]
+
+
+@pytest.mark.asyncio
+async def test_func_tool_return_list() -> None:
+    def my_function() -> List[int]:
+        return [1, 2]
+
+    tool = FunctionTool(my_function, description="Function tool.")
+    result = await tool.run_json({}, CancellationToken())
+    assert isinstance(result, list)
+    assert result == [1, 2]
+    assert tool.return_value_as_string(result) == "[1, 2]"