diff --git a/community_usecase/qwen3_mcp/run_mcp_qwen3.py b/community_usecase/qwen3_mcp/run_mcp_qwen3.py
index 04fa919..6fea3e5 100644
--- a/community_usecase/qwen3_mcp/run_mcp_qwen3.py
+++ b/community_usecase/qwen3_mcp/run_mcp_qwen3.py
@@ -13,11 +13,9 @@
 # ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
 import asyncio
 import sys
-import contextlib
 import time
 from pathlib import Path
 from typing import List, Dict, Any
-import os
 
 from colorama import Fore, init
 from dotenv import load_dotenv
@@ -26,7 +24,6 @@ from camel.agents.chat_agent import ToolCallingRecord
 from camel.models import ModelFactory
 from camel.toolkits import FunctionTool, MCPToolkit
 from camel.types import ModelPlatformType, ModelType
-from camel.logger import set_log_level
 
 from owl.utils.enhanced_role_playing import OwlRolePlaying
 
@@ -39,8 +36,6 @@ base_dir = pathlib.Path(__file__).parent.parent
 env_path = base_dir / "owl" / ".env"
 load_dotenv(dotenv_path=str(env_path))
 
-set_log_level(level="INFO")
-
 
 async def construct_society(
     question: str,
@@ -146,9 +141,6 @@ def write_to_md(filename: str, content: Dict[str, Any]) -> None:
         if "summary" in content:
             f.write(f"## Summary\n\n")
             f.write(f"{content['summary']}\n\n")
-            
-        if "token_count" in content:
-            f.write(f"**Total tokens used:** {content['token_count']}\n\n")
 
 
 async def run_society_with_formatted_output(society: OwlRolePlaying, md_filename: str, round_limit: int = 15):
@@ -182,12 +174,25 @@ async def run_society_with_formatted_output(society: OwlRolePlaying, md_filename
     
     input_msg = society.init_chat()
     chat_history = []
-    token_count = {"total": 0}
+    overall_completion_token_count = 0
+    overall_prompt_token_count = 0
     n = 0
     
     while n < round_limit:
         n += 1
         assistant_response, user_response = await society.astep(input_msg)
+
+        overall_completion_token_count += assistant_response.info["usage"].get(
+            "completion_tokens", 0
+        ) + user_response.info["usage"].get("completion_tokens", 0)
+        overall_prompt_token_count += assistant_response.info["usage"].get(
+            "prompt_tokens", 0
+        ) + user_response.info["usage"].get("prompt_tokens", 0)
+
+        token_info = {
+            "completion_token_count": overall_completion_token_count,
+            "prompt_token_count": overall_prompt_token_count,
+        }
         
         md_content = {}
         
@@ -239,10 +244,6 @@ async def run_society_with_formatted_output(society: OwlRolePlaying, md_filename
             "user": user_response.msg.content,
         })
         
-        # Update token count
-        if "token_count" in assistant_response.info:
-            token_count["total"] += assistant_response.info["token_count"]
-        
         if "TASK_DONE" in user_response.msg.content:
             task_done_msg = "Task completed successfully!"
             print(Fore.YELLOW + task_done_msg + "\n")
@@ -252,12 +253,12 @@ async def run_society_with_formatted_output(society: OwlRolePlaying, md_filename
         input_msg = assistant_response.msg
     
     # Write token count information
-    write_to_md(md_filename, {"token_count": token_count["total"]})
+    write_to_md(md_filename, token_info)
     
     # Extract final answer
     answer = assistant_response.msg.content if assistant_response and assistant_response.msg else ""
     
-    return answer, chat_history, token_count
+    return answer, chat_history, token_info
 
 
 async def main():
@@ -278,7 +279,7 @@ async def main():
     # Use command line argument if provided, otherwise use default task
     task = sys.argv[1] if len(sys.argv) > 1 else default_task
     
-    mcp_toolkit = MCPToolkit(config_path=str(config_path))
+    mcp_toolkit = MCPToolkit(config_path=str(config_path), strict=True)
     
     try:
         # Create markdown file for conversation export
@@ -294,10 +295,10 @@ async def main():
         # Build and run society
         print(Fore.YELLOW + f"Starting task: {task}\n")
         society = await construct_society(task, tools)
-        answer, chat_history, token_count = await run_society_with_formatted_output(society, md_filename)
+        answer, chat_history, token_info = await run_society_with_formatted_output(society, md_filename)
         
         print(Fore.GREEN + f"\nFinal Result: {answer}")
-        print(Fore.CYAN + f"Total tokens used: {token_count['total']}")
+        print(Fore.CYAN + f"Total tokens used: {token_info}")
         print(Fore.CYAN + f"Full conversation log saved to: {md_filename}")
             
     except KeyboardInterrupt: