fix(oci): preserve all parallel tool results in OCI GenAI messages

src/google/adk/integrations/oci/_oci_genai_llm.py

@@ -196,14 +196,25 @@ def _content_to_oci_message(content: types.Content) -> Any:
 
   role = _to_oci_role(content.role)
 
-  # Tool results map to ToolMessage (one per result)
+  # Tool results map to ToolMessage (one per result). Multiple parallel tool
+  # results are returned as a list of ToolMessages so none are dropped;
+  # mirror _content_to_message_param in lite_llm.py.
   if tool_results:
-    call_id, result_text = tool_results[0]
-    return oci_models.ToolMessage(
-        role=oci_models.ToolMessage.ROLE_TOOL,
-        tool_call_id=call_id,
-        content=[oci_models.TextContent(type="TEXT", text=result_text)],
-    )
+    if len(tool_results) == 1:
+      call_id, result_text = tool_results[0]
+      return oci_models.ToolMessage(
+          role=oci_models.ToolMessage.ROLE_TOOL,
+          tool_call_id=call_id,
+          content=[oci_models.TextContent(type="TEXT", text=result_text)],
+      )
+    return [
+        oci_models.ToolMessage(
+            role=oci_models.ToolMessage.ROLE_TOOL,
+            tool_call_id=call_id,
+            content=[oci_models.TextContent(type="TEXT", text=result_text)],
+        )
+        for call_id, result_text in tool_results
+    ]
 
   if role == "ASSISTANT":
     oci_content: list[Any] = []
@@ -451,7 +462,13 @@ def _build_chat_details(
     """Build OCI ChatDetails from an LlmRequest."""
     import oci.generative_ai_inference.models as oci_models
 
-    messages = [_content_to_oci_message(c) for c in llm_request.contents or []]
+    messages: list[Any] = []
+    for c in llm_request.contents or []:
+      msg = _content_to_oci_message(c)
+      if isinstance(msg, list):
+        messages.extend(msg)
+      else:
+        messages.append(msg)
 
     # Prepend SystemMessage when a system instruction is present
     if llm_request.config and llm_request.config.system_instruction:

tests/unittests/integrations/oci/test_oci_genai_llm.py

@@ -227,6 +227,50 @@ def test_content_to_oci_message_function_response():
   assert msg.content[0].text
 
 
+def test_content_to_oci_message_multiple_function_responses():
+  import oci.generative_ai_inference.models as oci_models
+
+  part_a = Part.from_function_response(
+      name="get_weather", response={"temp": 22}
+  )
+  part_a.function_response.id = "call_A"
+  part_b = Part.from_function_response(
+      name="get_price", response={"price": 150}
+  )
+  part_b.function_response.id = "call_B"
+  content = Content(role="user", parts=[part_a, part_b])
+  msg = _content_to_oci_message(content)
+  assert isinstance(msg, list)
+  assert len(msg) == 2
+  assert all(isinstance(m, oci_models.ToolMessage) for m in msg)
+  assert msg[0].tool_call_id == "call_A"
+  assert msg[1].tool_call_id == "call_B"
+  assert msg[0].content[0].text
+  assert msg[1].content[0].text
+
+
+def test_build_chat_details_flattens_multiple_tool_messages(oci_llm):
+  import oci.generative_ai_inference.models as oci_models
+
+  part_a = Part.from_function_response(
+      name="get_weather", response={"temp": 22}
+  )
+  part_a.function_response.id = "call_A"
+  part_b = Part.from_function_response(
+      name="get_price", response={"price": 150}
+  )
+  part_b.function_response.id = "call_B"
+  request = LlmRequest(
+      model="google.gemini-2.5-flash",
+      contents=[Content(role="user", parts=[part_a, part_b])],
+  )
+  chat_details = oci_llm._build_chat_details(request)
+  messages = chat_details.chat_request.messages
+  assert len(messages) == 2
+  assert all(m.role == oci_models.ToolMessage.ROLE_TOOL for m in messages)
+  assert [m.tool_call_id for m in messages] == ["call_A", "call_B"]
+
+
 # ---------------------------------------------------------------------------
 # _oci_response_to_llm_response
 # ---------------------------------------------------------------------------