Python: Fix workflow not pausing when agent calls declaration-only tool (#3757)

* Fix workflow not pausing when agent calls declaration-only tool

* Remove comment

python/samples/getting_started/workflows/README.md

@@ -84,6 +84,7 @@ Once comfortable with these, explore the rest of the samples below.
 | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------- |
 | Human-In-The-Loop (Guessing Game)          | [human-in-the-loop/guessing_game_with_human_input.py](./human-in-the-loop/guessing_game_with_human_input.py) | Interactive request/response prompts with a human via `ctx.request_info()`                            |
 | Agents with Approval Requests in Workflows | [human-in-the-loop/agents_with_approval_requests.py](./human-in-the-loop/agents_with_approval_requests.py)   | Agents that create approval requests during workflow execution and wait for human approval to proceed |
+| Agents with Declaration-Only Tools         | [human-in-the-loop/agents_with_declaration_only_tools.py](./human-in-the-loop/agents_with_declaration_only_tools.py) | Workflow pauses when agent calls a client-side tool (`func=None`), caller supplies the result         |
 | SequentialBuilder Request Info             | [human-in-the-loop/sequential_request_info.py](./human-in-the-loop/sequential_request_info.py)               | Request info for agent responses mid-workflow using `.with_request_info()` on SequentialBuilder       |
 | ConcurrentBuilder Request Info             | [human-in-the-loop/concurrent_request_info.py](./human-in-the-loop/concurrent_request_info.py)               | Review concurrent agent outputs before aggregation using `.with_request_info()` on ConcurrentBuilder  |
 | GroupChatBuilder Request Info              | [human-in-the-loop/group_chat_request_info.py](./human-in-the-loop/group_chat_request_info.py)               | Steer group discussions with periodic guidance using `.with_request_info()` on GroupChatBuilder       |

python/samples/getting_started/workflows/human-in-the-loop/agents_with_declaration_only_tools.py

@@ -0,0 +1,95 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Sample: Declaration-only tools in a workflow (issue #3425)
+
+A declaration-only tool (func=None) represents a client-side tool that the
+framework cannot execute — the LLM can call it, but the workflow must pause
+so the caller can supply the result.
+
+Flow:
+  1. The agent is given a declaration-only tool ("get_user_location").
+  2. When the LLM decides to call it, the workflow pauses and emits a
+     request_info event containing the FunctionCallContent.
+  3. The caller inspects the tool name/args, runs the tool however it wants,
+     and feeds the result back via workflow.run(responses={...}).
+  4. The workflow resumes — the agent sees the tool result and finishes.
+
+Prerequisites:
+  - Azure OpenAI endpoint configured via environment variables.
+  - `az login` for AzureCliCredential.
+"""
+
+import asyncio
+import json
+from typing import Any
+
+from agent_framework import Content, FunctionTool, WorkflowBuilder
+from agent_framework.azure import AzureOpenAIChatClient
+from azure.identity import AzureCliCredential
+
+# A declaration-only tool: the schema is sent to the LLM, but the framework
+# has no implementation to execute. The caller must supply the result.
+get_user_location = FunctionTool(
+    name="get_user_location",
+    func=None,
+    description="Get the user's current city. Only the client application can resolve this.",
+    input_model={
+        "type": "object",
+        "properties": {
+            "reason": {"type": "string", "description": "Why the location is needed"},
+        },
+        "required": ["reason"],
+    },
+)
+
+
+async def main() -> None:
+    agent = AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
+        name="WeatherBot",
+        instructions=(
+            "You are a helpful weather assistant. "
+            "When the user asks about weather, call get_user_location first, "
+            "then make up a plausible forecast for that city."
+        ),
+        tools=[get_user_location],
+    )
+
+    workflow = WorkflowBuilder(start_executor=agent).build()
+
+    # --- First run: the agent should call the declaration-only tool ---
+    print(">>> Sending: 'What's the weather like today?'")
+    result = await workflow.run("What's the weather like today?")
+
+    requests = result.get_request_info_events()
+    if not requests:
+        # The LLM chose not to call the tool — print whatever it said and exit
+        print(f"Agent replied without calling the tool: {result.get_outputs()}")
+        return
+
+    # --- Inspect what the agent wants ---
+    for req in requests:
+        data = req.data
+        args = json.loads(data.arguments) if isinstance(data.arguments, str) else data.arguments
+        print(f"Workflow paused — agent called: {data.name}({args})")
+
+    # --- "Execute" the tool on the client side and send results back ---
+    responses: dict[str, Any] = {}
+    for req in requests:
+        # In a real app this could be a GPS lookup, browser API, user prompt, etc.
+        client_result = "Seattle, WA"
+        print(f"Client provides result for {req.data.name}: {client_result!r}")
+        responses[req.request_id] = Content.from_function_result(
+            call_id=req.data.call_id,
+            result=client_result,
+        )
+
+    result = await workflow.run(responses=responses)
+
+    # --- Final answer ---
+    for output in result.get_outputs():
+        print(f"\nAgent: {output.text}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())