diff --git a/model_server/src/core/function_calling.py b/model_server/src/core/function_calling.py
index 1aa9ad46..cf2adb28 100644
--- a/model_server/src/core/function_calling.py
+++ b/model_server/src/core/function_calling.py
@@ -260,8 +260,7 @@ class ArchFunctionHandler(ArchBaseHandler):
 
         functions = {}
         for tool in tools:
-            if tool["type"] == "function":
-                functions[tool["function"]["name"]] = tool["function"]["parameters"]
+            functions[tool["function"]["name"]] = tool["function"]["parameters"]
 
         for tool_call in tool_calls:
             if not verification_dict["is_valid"]:
@@ -420,26 +419,28 @@ class ArchFunctionHandler(ArchBaseHandler):
                 if len(self.hallucination_state.tokens) > 5 and has_tool_calls is None:
                     content = "".join(self.hallucination_state.tokens)
                     if "tool_calls" in content:
+                        logger.info(
+                            f"[Content]: {content}"
+                        )
                         has_tool_calls = True
                     else:
                         has_tool_calls = False
-                        break
+
 
                 # if the model is hallucinating, start parameter gathering
                 if self.hallucination_state.hallucination is True:
                     has_hallucination = True
                     break
 
-            if has_tool_calls:
-                if has_hallucination:
-                    # start prompt prefilling if hallcuination is found in tool calls
-                    logger.info(
-                        f"[Hallucination]: {self.hallucination_state.error_message}"
-                    )
-                    prefill_response = self._engage_parameter_gathering(messages)
-                    model_response = prefill_response.choices[0].message.content
-                else:
-                    model_response = "".join(self.hallucination_state.tokens)
+            if has_tool_calls and has_hallucination:
+                # start prompt prefilling if hallcuination is found in tool calls
+                logger.info(
+                    f"[Hallucination]: {self.hallucination_state.error_message}"
+                )
+                prefill_response = self._engage_parameter_gathering(messages)
+                model_response = prefill_response.choices[0].message.content
+            else:
+                model_response = "".join(self.hallucination_state.tokens)
             # else:
             #     # start parameter gathering if the model is not generating tool calls
             #     prefill_response = self._engage_parameter_gathering(messages)