diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py
index 176c81d5d6..4ed9548566 100644
--- a/litellm/llms/prompt_templates/factory.py
+++ b/litellm/llms/prompt_templates/factory.py
@@ -481,10 +481,11 @@ def construct_tool_use_system_prompt(
 ):  # from https://github.com/anthropics/anthropic-cookbook/blob/main/function_calling/function_calling.ipynb
     tool_str_list = []
     for tool in tools:
+        tool_function = get_attribute_or_key(tool, "function")
         tool_str = construct_format_tool_for_claude_prompt(
-            tool["function"]["name"],
-            tool["function"].get("description", ""),
-            tool["function"].get("parameters", {}),
+            get_attribute_or_key(tool_function, "name"),
+            get_attribute_or_key(tool_function, "description", ""),
+            get_attribute_or_key(tool_function, "parameters", {}),
         )
         tool_str_list.append(tool_str)
     tool_use_system_prompt = (
@@ -608,7 +609,8 @@ def convert_to_anthropic_tool_result_xml(message: dict) -> str:
     </function_results>
     """
     name = message.get("name")
-    content = message.get("content")
+    content = message.get("content", "")
+    content = content.replace("<", "&lt;").replace(">", "&gt;").replace("&", "&amp;")
 
     # We can't determine from openai message format whether it's a successful or
     # error call result so default to the successful result template
@@ -629,13 +631,15 @@ def convert_to_anthropic_tool_result_xml(message: dict) -> str:
 def convert_to_anthropic_tool_invoke_xml(tool_calls: list) -> str:
     invokes = ""
     for tool in tool_calls:
-        if tool["type"] != "function":
+        if get_attribute_or_key(tool, "type") != "function":
             continue
 
-        tool_name = tool["function"]["name"]
+        tool_function =  get_attribute_or_key(tool,"function")
+        tool_name = get_attribute_or_key(tool_function, "name")
+        tool_arguments = get_attribute_or_key(tool_function, "arguments")
         parameters = "".join(
             f"<{param}>{val}</{param}>\n"
-            for param, val in json.loads(tool["function"]["arguments"]).items()
+            for param, val in json.loads(tool_arguments).items()
         )
         invokes += (
             "<invoke>\n"
@@ -689,7 +693,7 @@ def anthropic_messages_pt_xml(messages: list):
                     {
                         "type": "text",
                         "text": (
-                            convert_to_anthropic_tool_result(messages[msg_i])
+                            convert_to_anthropic_tool_result_xml(messages[msg_i])
                             if messages[msg_i]["role"] == "tool"
                             else messages[msg_i]["content"]
                         ),
@@ -710,7 +714,7 @@ def anthropic_messages_pt_xml(messages: list):
             if messages[msg_i].get(
                 "tool_calls", []
             ):  # support assistant tool invoke convertion
-                assistant_text += convert_to_anthropic_tool_invoke(  # type: ignore
+                assistant_text += convert_to_anthropic_tool_invoke_xml(  # type: ignore
                     messages[msg_i]["tool_calls"]
                 )
 
@@ -822,12 +826,12 @@ def convert_to_anthropic_tool_invoke(tool_calls: list) -> list:
     anthropic_tool_invoke = [
         {
             "type": "tool_use",
-            "id": tool["id"],
-            "name": tool["function"]["name"],
-            "input": json.loads(tool["function"]["arguments"]),
+            "id": get_attribute_or_key(tool, "id"),
+            "name": get_attribute_or_key(get_attribute_or_key(tool, "function"), "name"),
+            "input": json.loads(get_attribute_or_key(get_attribute_or_key(tool, "function"), "arguments")),
         }
         for tool in tool_calls
-        if tool["type"] == "function"
+        if get_attribute_or_key(tool, "type") == "function"
     ]
 
     return anthropic_tool_invoke
@@ -1370,3 +1374,8 @@ def prompt_factory(
         return default_pt(
             messages=messages
         )  # default that covers Bloom, T-5, any non-chat tuned model (e.g. base Llama2)
+
+def get_attribute_or_key(tool_or_function, attribute, default=None):
+    if hasattr(tool_or_function, attribute):
+        return getattr(tool_or_function, attribute)
+    return tool_or_function.get(attribute, default)
diff --git a/litellm/tests/test_bedrock_completion.py b/litellm/tests/test_bedrock_completion.py
index 4b1781cd93..ca2ffea5f5 100644
--- a/litellm/tests/test_bedrock_completion.py
+++ b/litellm/tests/test_bedrock_completion.py
@@ -269,6 +269,30 @@ def test_bedrock_claude_3_tool_calling():
         assert isinstance(
             response.choices[0].message.tool_calls[0].function.arguments, str
         )
+        messages.append(
+            response.choices[0].message.model_dump()
+        )  # Add assistant tool invokes
+        tool_result = (
+            '{"location": "Boston", "temperature": "72", "unit": "fahrenheit"}'
+        )
+        # Add user submitted tool results in the OpenAI format
+        messages.append(
+            {
+                "tool_call_id": response.choices[0].message.tool_calls[0].id,
+                "role": "tool",
+                "name": response.choices[0].message.tool_calls[0].function.name,
+                "content": tool_result,
+            }
+        )
+        # In the second response, Claude should deduce answer from tool results
+        second_response = completion(
+            model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+            messages=messages,
+            tools=tools,
+            tool_choice="auto",
+        )
+        print(f"second response: {second_response}")
+        assert isinstance(second_response.choices[0].message.content, str)
     except RateLimitError:
         pass
     except Exception as e: