diff --git a/litellm/llms/anthropic.py b/litellm/llms/anthropic.py
index b6200a1a42..864ad658fb 100644
--- a/litellm/llms/anthropic.py
+++ b/litellm/llms/anthropic.py
@@ -3,7 +3,7 @@ import json
 from enum import Enum
 import requests, copy
 import time, uuid
-from typing import Callable, Optional
+from typing import Callable, Optional, List
 from litellm.utils import ModelResponse, Usage, map_finish_reason, CustomStreamWrapper
 import litellm
 from .prompt_templates.factory import (
@@ -118,6 +118,7 @@ def completion(
 ):
     headers = validate_environment(api_key, headers)
     _is_function_call = False
+    json_schemas: dict = {}
     messages = copy.deepcopy(messages)
     optional_params = copy.deepcopy(optional_params)
     if model in custom_prompt_dict:
@@ -161,6 +162,10 @@ def completion(
     ## Handle Tool Calling
     if "tools" in optional_params:
         _is_function_call = True
+        for tool in optional_params["tools"]:
+            json_schemas[tool["function"]["name"]] = tool["function"].get(
+                "parameters", None
+            )
         tool_calling_system_prompt = construct_tool_use_system_prompt(
             tools=optional_params["tools"]
         )
@@ -248,7 +253,12 @@ def completion(
                     0
                 ].strip()
                 function_arguments_str = f"<invoke>{function_arguments_str}</invoke>"
-                function_arguments = parse_xml_params(function_arguments_str)
+                function_arguments = parse_xml_params(
+                    function_arguments_str,
+                    json_schema=json_schemas.get(
+                        function_name, None
+                    ),  # check if we have a json schema for this function name
+                )
                 _message = litellm.Message(
                     tool_calls=[
                         {
@@ -263,6 +273,9 @@ def completion(
                     content=None,
                 )
                 model_response.choices[0].message = _message  # type: ignore
+                model_response._hidden_params["original_response"] = (
+                    text_content  # allow user to access raw anthropic tool calling response
+                )
             else:
                 model_response.choices[0].message.content = text_content  # type: ignore
             model_response.choices[0].finish_reason = map_finish_reason(
diff --git a/litellm/llms/bedrock.py b/litellm/llms/bedrock.py
index 8177167a55..ae78d3ec48 100644
--- a/litellm/llms/bedrock.py
+++ b/litellm/llms/bedrock.py
@@ -691,6 +691,7 @@ def completion(
 ):
     exception_mapping_worked = False
     _is_function_call = False
+    json_schemas: dict = {}
     try:
         # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
         aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
@@ -757,6 +758,10 @@ def completion(
                 ## Handle Tool Calling
                 if "tools" in inference_params:
                     _is_function_call = True
+                    for tool in inference_params["tools"]:
+                        json_schemas[tool["function"]["name"]] = tool["function"].get(
+                            "parameters", None
+                        )
                     tool_calling_system_prompt = construct_tool_use_system_prompt(
                         tools=inference_params["tools"]
                     )
@@ -943,7 +948,12 @@ def completion(
                     function_arguments_str = (
                         f"<invoke>{function_arguments_str}</invoke>"
                     )
-                    function_arguments = parse_xml_params(function_arguments_str)
+                    function_arguments = parse_xml_params(
+                        function_arguments_str,
+                        json_schema=json_schemas.get(
+                            function_name, None
+                        ),  # check if we have a json schema for this function name)
+                    )
                     _message = litellm.Message(
                         tool_calls=[
                             {
@@ -958,6 +968,9 @@ def completion(
                         content=None,
                     )
                     model_response.choices[0].message = _message  # type: ignore
+                    model_response._hidden_params["original_response"] = (
+                        outputText  # allow user to access raw anthropic tool calling response
+                    )
                 if _is_function_call == True and stream is not None and stream == True:
                     print_verbose(
                         f"INSIDE BEDROCK STREAMING TOOL CALLING CONDITION BLOCK"
diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py
index 90bc64161c..4492423f45 100644
--- a/litellm/llms/prompt_templates/factory.py
+++ b/litellm/llms/prompt_templates/factory.py
@@ -731,18 +731,53 @@ def contains_tag(tag: str, string: str) -> bool:
     return bool(re.search(f"<{tag}>(.+?)</{tag}>", string, re.DOTALL))
 
 
-def parse_xml_params(xml_content):
+def parse_xml_params(xml_content, json_schema: Optional[dict] = None):
+    """
+    Compare the xml output to the json schema
+
+    check if a value is a list - if so, get it's child elements
+    """
     root = ET.fromstring(xml_content)
     params = {}
-    for child in root.findall(".//parameters/*"):
-        try:
-            # Attempt to decode the element's text as JSON
-            params[child.tag] = json.loads(child.text)
-        except json.JSONDecodeError:
-            # If JSON decoding fails, use the original text
-            params[child.tag] = child.text
+
+    if json_schema is not None:  # check if we have a json schema for this function call
+        # iterate over all properties in the schema
+        for prop in json_schema["properties"]:
+            # If property is an array, get the nested items
+            _element = root.find(f"parameters/{prop}")
+            if json_schema["properties"][prop]["type"] == "array":
+                items = []
+                if _element is not None:
+                    for value in _element:
+                        try:
+                            if value.text is not None:
+                                _value = json.loads(value.text)
+                            else:
+                                continue
+                        except json.JSONDecodeError:
+                            _value = value.text
+                        items.append(_value)
+                    params[prop] = items
+            # If property is not an array, append the value directly
+            elif _element is not None and _element.text is not None:
+                try:
+                    _value = json.loads(_element.text)
+                except json.JSONDecodeError:
+                    _value = _element.text
+                params[prop] = _value
+    else:
+        for child in root.findall(".//parameters/*"):
+            if child is not None and child.text is not None:
+                try:
+                    # Attempt to decode the element's text as JSON
+                    params[child.tag] = json.loads(child.text)  # type: ignore
+                except json.JSONDecodeError:
+                    # If JSON decoding fails, use the original text
+                    params[child.tag] = child.text  # type: ignore
+
     return params
 
+
 ###
 
 
diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py
index c27dba4791..cb4ee84b57 100644
--- a/litellm/tests/test_completion.py
+++ b/litellm/tests/test_completion.py
@@ -195,6 +195,48 @@ def test_completion_claude_3_function_call():
         pytest.fail(f"Error occurred: {e}")
 
 
+def test_parse_xml_params():
+    from litellm.llms.prompt_templates.factory import parse_xml_params
+
+    ## SCENARIO 1 ## - W/ ARRAY
+    xml_content = """<invoke><tool_name>return_list_of_str</tool_name>\n<parameters>\n<value>\n<item>apple</item>\n<item>banana</item>\n<item>orange</item>\n</value>\n</parameters></invoke>"""
+    json_schema = {
+        "properties": {
+            "value": {
+                "items": {"type": "string"},
+                "title": "Value",
+                "type": "array",
+            }
+        },
+        "required": ["value"],
+        "type": "object",
+    }
+    response = parse_xml_params(xml_content=xml_content, json_schema=json_schema)
+
+    print(f"response: {response}")
+    assert response["value"] == ["apple", "banana", "orange"]
+
+    ## SCENARIO 2 ## - W/OUT ARRAY
+    xml_content = """<invoke><tool_name>get_current_weather</tool_name>\n<parameters>\n<location>Boston, MA</location>\n<unit>fahrenheit</unit>\n</parameters></invoke>"""
+    json_schema = {
+        "type": "object",
+        "properties": {
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA",
+            },
+            "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+        },
+        "required": ["location"],
+    }
+
+    response = parse_xml_params(xml_content=xml_content, json_schema=json_schema)
+
+    print(f"response: {response}")
+    assert response["location"] == "Boston, MA"
+    assert response["unit"] == "fahrenheit"
+
+
 def test_completion_claude_3_multi_turn_conversations():
     litellm.set_verbose = True
     litellm.modify_params = True