fix(azure_ai): drop tool_reference parts from tool messages on chat (#48032)

litellm/llms/azure_ai/chat/transformation.py

@@ -12,6 +12,7 @@ from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     audio_or_image_in_message_content,
     convert_content_list_to_str,
+    drop_tool_reference_parts_from_tool_messages,
     filter_value_from_dict,
 )
 from litellm.llms.azure.common_utils import BaseAzureLLM
@@ -250,7 +251,7 @@ class AzureAIStudioConfig(OpenAIConfig):
         Operates on a deep copy so the caller's messages keep their thinking blocks
         and provider metadata, which a fallback to another provider still needs.
         """
-        stripped_messages: Final = copy.deepcopy(messages)
+        stripped_messages: Final = copy.deepcopy(drop_tool_reference_parts_from_tool_messages(messages))
         for message in stripped_messages:
             message_dict = cast(dict, message)  # cast-ok: TypedDict is a runtime dict stripped on our copy
             for field in NON_OPENAI_SPEC_MESSAGE_FIELDS:

tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py

@@ -879,6 +879,32 @@ def test_azure_ai_stripping_does_not_mutate_caller_messages():
     }
 
 
+def test_azure_ai_reference_only_tool_result_is_sent_as_empty_text():
+    messages: Final = [
+        {"role": "user", "content": "load the WebFetch tool"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [{"id": "call_1", "type": "function", "function": {"name": "ToolSearch", "arguments": "{}"}}],
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_1",
+            "content": [{"type": "tool_reference", "tool_name": "WebFetch"}],
+        },
+    ]
+
+    request: Final = AzureAIStudioConfig().transform_request(
+        model="fw-glm-5.2",
+        messages=messages,
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+
+    assert request["messages"][2] == {"role": "tool", "tool_call_id": "call_1", "content": ""}
+
+
 @pytest.mark.parametrize(
     "api_base, expected_url",
     [