[BREAKING] Python: clean up kwargs across agents, chat clients, tools, and sessions (#4581)

* Python: clean up kwargs across agents, chat clients, tools, and sessions (#3642)

Audit and refactor public **kwargs usage across core agents, chat clients,
tools, sessions, and provider packages per the migration strategy codified
in CODING_STANDARD.md.

Key changes:
- Add explicit runtime buckets: function_invocation_kwargs and client_kwargs
  on RawAgent.run() and chat client get_response() layers.
- Refactor FunctionTool to prefer explicit ctx: FunctionInvocationContext
  injection; legacy **kwargs tools still work via _forward_runtime_kwargs.
- Refactor Agent.as_tool() to use direct JSON schema, always-streaming
  wrapper, approval_mode parameter, and UserInputRequiredException
  propagation (integrates PR #4568 behavior).
- Remove implicit session bleeding into FunctionInvocationContext; tools
  that need a session must receive it via function_invocation_kwargs.
- Lower chat-client layers after FunctionInvocationLayer accept only
  compatibility **kwargs (client_kwargs flattened, function_invocation_kwargs
  ignored).
- Add layered docstring composition from Raw... implementations via
  _docstrings.py helper.
- Clean up provider constructors to use explicit additional_properties.
- Deprecation warnings on legacy direct kwargs paths.
- Update samples, tests, and typing across all 23 packages.

Resolves #3642

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* clarified docstring

* feedback fixes

* Add unit tests for _docstrings.py build/apply helpers

Tests cover: no docstring source, no extra kwargs, appending to existing
Keyword Args section, inserting after Args, inserting in plain docstrings,
multiline descriptions, ordering, and apply_layered_docstring.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Add test for propagate_session TypeError on non-AgentSession values

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Add tests for multi-content and empty UserInputRequiredException propagation

Cover the branching logic in _try_execute_function_calls for:
- Multiple user_input_request items in a single exception (extra_user_input_contents path)
- Empty contents list (fallback function_result path)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Add tests for DurableAIAgent.get_session forwarding service_session_id

Verifies get_session correctly forwards service_session_id and session_id
to the executor's get_new_session, replacing the removed kwargs test.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Simplify ag-ui test stub to read session from client_kwargs only

Remove dual-mode detection (client_kwargs vs raw kwargs fallback) from
the test mock. Session is now read exclusively from client_kwargs,
matching the settled public calling convention.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* updated create and get sessions in durable

* fixed docstrings

* fix test

* updated session handling

* updated from main

* updated tests

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

python/samples/02-agents/tools/function_tool_with_kwargs.py

@@ -1,9 +1,9 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
-from typing import Annotated, Any
+from typing import Annotated
 
-from agent_framework import tool
+from agent_framework import FunctionInvocationContext, tool
 from agent_framework.openai import OpenAIResponsesClient
 from dotenv import load_dotenv
 from pydantic import Field
@@ -14,27 +14,27 @@ load_dotenv()
 """
 AI Function with kwargs Example
 
-This example demonstrates how to inject custom keyword arguments (kwargs) into an AI function
-from the agent's run method, without exposing them to the AI model.
+This example demonstrates how to inject runtime context into an AI function
+from the agent's run method, without exposing it to the AI model.
 
 This is useful for passing runtime information like access tokens, user IDs, or
 request-specific context that the tool needs but the model shouldn't know about
-or provide.
+or provide. The injected context parameter can be typed as
+``FunctionInvocationContext`` as shown here, or left untyped as ``ctx`` when you
+prefer a lighter-weight sample setup.
 """
 
 
-# Define the function tool with **kwargs to accept injected arguments
-# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
-# see samples/02-agents/tools/function_tool_with_approval.py
-# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
+# Define the function tool with explicit invocation context.
+# The context parameter can also be declared as an untyped ``ctx`` parameter.
 @tool(approval_mode="never_require")
 def get_weather(
     location: Annotated[str, Field(description="The location to get the weather for.")],
-    **kwargs: Any,
+    ctx: FunctionInvocationContext,
 ) -> str:
     """Get the weather for a given location."""
-    # Extract the injected argument from kwargs
-    user_id = kwargs.get("user_id", "unknown")
+    # Extract the injected argument from the explicit context
+    user_id = ctx.kwargs.get("user_id", "unknown")
 
     # Simulate using the user_id for logging or personalization
     print(f"Getting weather for user: {user_id}")
@@ -49,9 +49,11 @@ async def main() -> None:
         tools=[get_weather],
     )
 
-    # Pass the injected argument when running the agent
-    # The 'user_id' kwarg will be passed down to the tool execution via **kwargs
-    response = await agent.run("What is the weather like in Amsterdam?", user_id="user_123")
+    # Pass the runtime context explicitly when running the agent.
+    response = await agent.run(
+        "What is the weather like in Amsterdam?",
+        function_invocation_kwargs={"user_id": "user_123"},
+    )
 
     print(f"Agent: {response.text}")