diff --git a/python/semantic_kernel/functions/kernel_function.py b/python/semantic_kernel/functions/kernel_function.py
index fee32dd437c1..bf5b81c015a2 100644
--- a/python/semantic_kernel/functions/kernel_function.py
+++ b/python/semantic_kernel/functions/kernel_function.py
@@ -430,6 +430,7 @@ def as_agent_framework_tool(
         Returns:
             AIFunction: The agent framework tool.
         """
+        import inspect
         import json
 
         from pydantic import Field, create_model
@@ -448,15 +449,60 @@ def as_agent_framework_tool(
             kernel = Kernel()
         name = name or self.name
         description = description or self.description
+
+        # `param.is_required` is False for any annotation that contains None (e.g. both
+        # `x: str | None` and `x: str | None = None`), even though only the second one
+        # actually has a default. Without correcting for that, a parameter that Python
+        # itself still requires would be built as optional here, and invoking the tool
+        # with it omitted would raise a raw TypeError from the underlying call instead of
+        # failing input validation the way a missing required field should. When the
+        # function has a real Python callable, recover the distinction from its signature.
+        method = getattr(self, "method", None)
+        signature = inspect.signature(method) if method is not None else None
+
         fields = {}
         for param in self.parameters:
             if param.include_in_function_choices:
-                if param.default_value is not None:
+                sig_param = signature.parameters.get(param.name) if signature is not None and param.name else None
+                is_required = param.is_required or (
+                    sig_param is not None and sig_param.default is inspect.Parameter.empty
+                )
+                # _process_signature stores inspect.Parameter.empty as type_object for an
+                # unannotated parameter, and pydantic cannot build a schema from that, so it
+                # counts the same as "no usable type object" alongside None.
+                type_object = param.type_object
+                if type_object is inspect.Parameter.empty:
+                    type_object = None
+                annotation: Any = param.type_
+                if is_required:
+                    if not param.is_required and type_object is not None:
+                        # Only the signature makes it required: the annotation itself includes
+                        # None (e.g. `x: str | None` with no default), so an explicit None
+                        # should still validate, and the schema should say so.
+                        annotation = type_object | None
+                    fields[param.name] = (annotation, Field(description=param.description))
+                else:
+                    # A parameter that isn't required may still carry an actual default of None
+                    # (e.g. `tag: str | None = None`), which is different from having no default
+                    # at all. The annotation needs to accept None in that case, or pydantic
+                    # rejects the None default and any explicit None passed by the caller.
+                    if type_object is not None:
+                        # Prefer the real type object when we have one: it is unambiguous,
+                        # unlike the type_ string, which turns a multi-type union such as
+                        # `str | int | None` into "str, int", a string pydantic cannot evaluate.
+                        annotation = type_object
+                        if param.default_value is None:
+                            annotation = type_object | None
+                    else:
+                        # type_ is a comma-joined string for a multi-type union (e.g. "str, int")
+                        # when no single non-None type could be resolved; appending "| None" to
+                        # that would produce an invalid forward reference, so it is left alone.
+                        if param.default_value is None and param.type_ and "," not in param.type_:
+                            annotation = f"{param.type_} | None"
                     fields[param.name] = (
-                        param.type_,
+                        annotation,
                         Field(description=param.description, default=param.default_value),
                     )
-                fields[param.name] = (param.type_, Field(description=param.description))
         input_model = create_model("InputModel", **fields)  # type: ignore
 
         async def wrapper(*args: Any, **kwargs: Any) -> Any:
diff --git a/python/tests/unit/functions/test_kernel_function_from_method.py b/python/tests/unit/functions/test_kernel_function_from_method.py
index 377fae16066f..a1066ae8c466 100644
--- a/python/tests/unit/functions/test_kernel_function_from_method.py
+++ b/python/tests/unit/functions/test_kernel_function_from_method.py
@@ -1,13 +1,17 @@
 # Copyright (c) Microsoft. All rights reserved.
 import logging
+import sys
+import types
 from collections.abc import AsyncGenerator, Iterable
 from contextlib import nullcontext
+from dataclasses import dataclass
 from importlib import import_module
 from typing import Annotated, Any
 from unittest.mock import Mock, patch
 
 import pytest
 from opentelemetry.trace import StatusCode
+from pydantic import ValidationError
 
 from semantic_kernel.connectors.ai.open_ai.services.open_ai_chat_completion import OpenAIChatCompletion
 from semantic_kernel.exceptions import FunctionExecutionException, FunctionInitializationError
@@ -681,3 +685,150 @@ def test_function_model_dump_json(get_custom_type_function_pydantic):
     model_dump = func.model_dump_json()
     assert isinstance(model_dump, str)
     assert "metadata" in model_dump
+
+
+@pytest.fixture
+def stub_agent_framework(monkeypatch):
+    """Stub the optional `agent_framework` dependency so as_agent_framework_tool can be exercised."""
+
+    class AIFunction:
+        def __init__(self, *, name, description, input_model, func):
+            self.name = name
+            self.description = description
+            self.input_model = input_model
+            self.func = func
+
+    module = types.ModuleType("agent_framework")
+    module.AIFunction = AIFunction
+    monkeypatch.setitem(sys.modules, "agent_framework", module)
+
+
+def test_as_agent_framework_tool_keeps_defaults_and_required(stub_agent_framework):
+    @kernel_function(name="search", description="search things")
+    def search(
+        query: Annotated[str, "search query"],
+        top_k: Annotated[int, "number of results"] = 5,
+        include_meta: Annotated[bool, "include metadata"] = False,
+        offset: Annotated[int, "results to skip"] = 0,
+        label: Annotated[str, "label"] = "",
+        y=3,  # unannotated parameter with a default: type_object is inspect.Parameter.empty
+    ) -> str:
+        return f"{query}:{top_k}:{include_meta}:{offset}:{label}:{y}"
+
+    func = KernelFunction.from_method(search, "test")
+    tool = func.as_agent_framework_tool()
+    schema = tool.input_model.model_json_schema()
+
+    # Only the parameter without a default should be required.
+    assert schema.get("required") == ["query"]
+    # Falsy defaults (0, False, "") must survive, not be dropped as if they were None.
+    assert schema["properties"]["top_k"]["default"] == 5
+    assert schema["properties"]["include_meta"]["default"] is False
+    assert schema["properties"]["offset"]["default"] == 0
+    assert schema["properties"]["label"]["default"] == ""
+    # An unannotated parameter with a default must not crash tool creation (its
+    # type_object is inspect.Parameter.empty, which pydantic cannot build a schema
+    # from) and must fall back to being optional with its default preserved.
+    assert schema["properties"]["y"]["default"] == 3
+    assert "y" not in schema.get("required", [])
+
+
+def test_as_agent_framework_tool_none_defaults_accept_none(stub_agent_framework):
+    @kernel_function(name="configure", description="configure things")
+    def configure(
+        q: Annotated[str, "required query"],
+        tag: Annotated[str | None, "tag"] = None,
+        n: Annotated[int | None, "n"] = None,
+    ) -> str:
+        return f"{q}:{tag}:{n}"
+
+    func = KernelFunction.from_method(configure, "test")
+    tool = func.as_agent_framework_tool()
+    schema = tool.input_model.model_json_schema()
+
+    # Only the parameter without any default is required; a None default still
+    # makes a parameter optional, it must not be conflated with "no default".
+    assert schema.get("required") == ["q"]
+    assert schema["properties"]["tag"]["default"] is None
+    assert schema["properties"]["n"]["default"] is None
+
+    # The model must accept both an omitted value and an explicit None for the
+    # None-defaulted parameters.
+    instance = tool.input_model(q="x")
+    assert instance.tag is None
+    assert instance.n is None
+
+    instance_explicit = tool.input_model(q="x", tag=None, n=None)
+    assert instance_explicit.tag is None
+    assert instance_explicit.n is None
+
+
+@dataclass
+class _CustomType:
+    id: str = ""
+
+
+def test_as_agent_framework_tool_none_default_generic_and_model_types(stub_agent_framework):
+    """A None-defaulted `list[str]` or dataclass parameter has a real
+    `type_object`, unlike a plain string, so the annotation can be built from it
+    directly (`param.type_object | None`) instead of the type_ string.
+    """
+
+    @kernel_function(name="configure", description="configure things")
+    def configure(
+        q: Annotated[str, "required query"],
+        b: Annotated[list[str] | None, "b"] = None,
+        c: Annotated[_CustomType | None, "c"] = None,
+    ) -> str:
+        return f"{q}:{b}:{c}"
+
+    func = KernelFunction.from_method(configure, "test")
+    tool = func.as_agent_framework_tool()
+    schema = tool.input_model.model_json_schema()
+
+    assert schema.get("required") == ["q"]
+    assert schema["properties"]["b"]["default"] is None
+    assert schema["properties"]["c"]["default"] is None
+
+    instance = tool.input_model(q="x")
+    assert instance.b is None
+    assert instance.c is None
+
+    instance_explicit = tool.input_model(q="x", b=["y"], c=_CustomType(id="z"))
+    assert instance_explicit.b == ["y"]
+    assert instance_explicit.c == _CustomType(id="z")
+
+
+def test_as_agent_framework_tool_optional_annotation_without_default_stays_required(stub_agent_framework):
+    """`x: str | None` (no default) still makes `is_required=False` in the decorator's own
+    metadata, because that only checks whether the annotation contains None, not whether the
+    Python signature has a default. Python itself still requires `x`, so the bridge recovers
+    that from the real callable's signature and keeps `x` required, while `y: str | None = None`,
+    which genuinely has a default, stays optional.
+    """
+
+    @kernel_function(name="probe", description="probe things")
+    def probe(x: Annotated[str | None, "x, no default"], y: Annotated[str | None, "y, defaulted"] = None) -> str:
+        return f"{x}:{y}"
+
+    func = KernelFunction.from_method(probe, "test")
+    assert func.parameters[0].is_required is False  # the decorator's own metadata, unchanged
+
+    tool = func.as_agent_framework_tool()
+    schema = tool.input_model.model_json_schema()
+
+    assert schema.get("required") == ["x"]
+    assert "y" not in (schema.get("required") or [])
+    assert schema["properties"]["y"]["default"] is None
+
+    with pytest.raises(ValidationError):
+        tool.input_model()
+
+    instance = tool.input_model(x="hi")
+    assert instance.x == "hi"
+    assert instance.y is None
+
+    # x is required (no default in the signature), but its annotation still includes
+    # None, so an explicit null must validate rather than be rejected as the wrong type.
+    instance_explicit_none = tool.input_model(x=None)
+    assert instance_explicit_none.x is None