diff --git a/tests/entrypoints/openai/test_completion_with_function_calling.py b/tests/entrypoints/openai/test_completion_with_function_calling.py
index 3649cefa9bf4..4355603fcd70 100644
--- a/tests/entrypoints/openai/test_completion_with_function_calling.py
+++ b/tests/entrypoints/openai/test_completion_with_function_calling.py
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import datetime
 from typing import Union
 
 import openai  # use the official client for correctness check
@@ -284,3 +285,62 @@ async def test_tool_id_kimi_k2(k2_client: openai.AsyncOpenAI, model_name: str,
                 output.extend(chunk.choices[0].delta.tool_calls)
         for o in output:
             assert o.id is None or o.id == 'functions.get_current_weather:0'
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize("arguments", ["{}", ''])
+async def test_no_args_tool_call(client: openai.AsyncOpenAI, model_name: str,
+                                 arguments: str):
+    # Step 1: Define a tool that requires no parameters
+    tools = [{
+        "type": "function",
+        "function": {
+            "name": "get_current_time",
+            "description":
+            "Get the current date and time. No parameters needed.",
+            "parameters": {
+                "type": "object",
+                "properties": {},  # No parameters
+                "required": []  # No required fields
+            }
+        }
+    }]
+    messages = [{"role": "user", "content": "What time is it now?"}]
+    # Step 2: Send user message and let model decide whether to call the tool
+    response = await client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        tools=tools,
+        tool_choice="auto"  # Let model choose automatically
+    )
+
+    # Step 3: Check if model wants to call a tool
+    message = response.choices[0].message
+    if message.tool_calls:
+        # Get the first tool call
+        tool_call = message.tool_calls[0]
+        tool_name = tool_call.function.name
+        # Step 4: Execute the tool locally (no parameters)
+        if tool_name == "get_current_time":
+            # Test both empty string and "{}" for no-arg tool calls
+            tool_call.function.arguments = arguments
+            messages.append(message)
+            current_time = datetime.datetime.now()
+            result = current_time.isoformat()
+            messages.append({
+                "role": "tool",
+                "tool_call_id": tool_call.id,
+                "content": result,
+            })
+            # Step 5: Send tool result back to model to continue conversation
+            final_response = await client.chat.completions.create(
+                model=model_name,
+                messages=messages,
+            )
+            # Output final natural language response
+            assert final_response.choices[0].message.content is not None
+
+    else:
+        # No tool called — just print model's direct reply
+        assert message.content is not None
diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py
index 00ef39f13465..c2c0ad74ef43 100644
--- a/vllm/entrypoints/chat_utils.py
+++ b/vllm/entrypoints/chat_utils.py
@@ -1450,9 +1450,11 @@ def _postprocess_messages(messages: list[ConversationMessage]) -> None:
             and isinstance(message["tool_calls"], list)
         ):
             for item in message["tool_calls"]:
-                item["function"]["arguments"] = json.loads(
-                    item["function"]["arguments"]
-                )
+                # if arguments is None or empty string, set to {}
+                if content := item["function"].get("arguments"):
+                    item["function"]["arguments"] = json.loads(content)
+                else:
+                    item["function"]["arguments"] = {}
 
 
 def parse_chat_messages(