diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py
index 0ce7e63488c..595ce96e534 100644
--- a/litellm/llms/openai/chat/guardrail_translation/handler.py
+++ b/litellm/llms/openai/chat/guardrail_translation/handler.py
@@ -794,6 +794,14 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
         self.merge_user_api_key_metadata_into_request(request_data, user_api_key_dict)
 
         inputs: Final = GenericGuardrailAPIInputs(texts=texts_to_check)
+        if self._streamed_tool_call_fingerprints(responses_so_far):
+            assembled: Final = self._rebuild_ended_stream_per_choice(responses_so_far, litellm_logging_obj)
+            inputs["tool_calls"] = [
+                converted
+                for choice in assembled.choices
+                for tool_call in choice.message.tool_calls or ()
+                if (converted := self._convert_tool_call_to_dict(tool_call)) is not None
+            ]
         if responses_so_far and getattr(responses_so_far[0], "model", None):
             inputs["model"] = responses_so_far[0].model
         guardrailed_inputs: Final = await guardrail_to_apply.apply_guardrail(
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py
index c13625296f8..98269add1ad 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py
@@ -946,10 +946,11 @@ def _delta_text(item):
 
 
 class _ToolRedactingGuardrail(CustomGuardrail):
-    def __init__(self) -> None:
+    def __init__(self, require_tool_context: bool = False) -> None:
         super().__init__(guardrail_name="tool-redactor", event_hook=GuardrailEventHooks.post_call, default_on=True)
         self.streaming_transform_mode = "incremental_diff"
         self.streaming_sampling_rate = 1
+        self.require_tool_context = require_tool_context
 
     async def apply_guardrail(
         self,
@@ -961,7 +962,10 @@ class _ToolRedactingGuardrail(CustomGuardrail):
         calls: Final = TypeAdapter(tuple[ChatCompletionMessageToolCall, ...]).validate_python(
             inputs.get("tool_calls", ())
         )
-        texts: Final = tuple("checked:" + text.replace("SECRET", "MASKED") for text in inputs.get("texts", ()))
+        texts: Final = tuple(
+            "checked:" + text.replace("SECRET", "MASKED") if calls or not self.require_tool_context else text
+            for text in inputs.get("texts", ())
+        )
         return {
             **inputs,
             "texts": list(texts),
@@ -984,10 +988,11 @@ class TestStreamingTransform:
         _patch_translation_mappings(monkeypatch, {CallTypes.acompletion: OpenAIChatCompletionsHandler})
 
     @pytest.mark.asyncio
+    @pytest.mark.parametrize("require_tool_context", [False, True])
     @pytest.mark.parametrize("include_text", [False, True])
     @pytest.mark.parametrize("tool_count", [1, 2])
     async def test_buffered_tool_arguments_are_rewritten_before_delivery(
-        self, include_text: bool, tool_count: int
+        self, include_text: bool, tool_count: int, require_tool_context: bool
     ) -> None:
         chunks: Final = (
             *([_stream_chunk("hello SECRET")] if include_text else []),
@@ -1001,7 +1006,9 @@ class TestStreamingTransform:
             ]), finish_reason="tool_calls")]),
             ModelResponseStream(choices=[], usage={"prompt_tokens": 7, "completion_tokens": 11, "total_tokens": 18}),
         )
-        out: Final = await _drive_stream(UnifiedLLMGuardrails(), _ToolRedactingGuardrail(), chunks)
+        out: Final = await _drive_stream(
+            UnifiedLLMGuardrails(), _ToolRedactingGuardrail(require_tool_context=require_tool_context), chunks
+        )
         calls: Final = tuple(
             call for chunk in out for choice in chunk.choices for call in choice.delta.tool_calls or ()
         )