refactor(aibox): 工具基类 Tool/ToolRegistry 抽离至 tools 包

延续 common 包的拆分模式，形成 common <- tools <- aibox 单向依赖，
为后续工具子类逐步迁移铺路；aibox.py 瘦身约 230 行，工具子类与注册逻辑零改动。

aibox/tools/base.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+"""
+tools.base - 工具抽象基类
+
+抽离自 aibox.py 的 Tool 基类。仅依赖 common 与标准库，
+不反向依赖 aibox 任何符号。
+"""
+
+import os
+import queue
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional, Tuple
+
+from common import ConfigManager, Logger, constants
+
+
+class Tool(ABC):
+    """工具基类"""
+    
+    def __init__(self, logger: Optional[Logger] = None):
+        self.logger = logger or Logger(self.__class__.__name__)
+        self.config = ConfigManager()
+        self.streaming_output = False  # 标记是否正在流式输出
+        self.output_queue = None  # 添加输出队列属性
+    
+    @abstractmethod
+    def get_name(self) -> str:
+        """返回工具名称"""
+        pass
+    
+    @abstractmethod
+    def get_description(self) -> str:
+        """返回工具描述"""
+        pass
+    
+    @abstractmethod
+    def get_parameters(self) -> Dict:
+        """返回工具参数定义（JSON Schema格式）"""
+        pass
+    
+    @abstractmethod
+    def execute(self, **kwargs) -> str:
+        """执行工具并返回结果"""
+        pass
+    
+    def execute_streaming(self, output_queue: queue.Queue, **kwargs):
+        """流式执行工具，通过队列输出结果"""
+        self.output_queue = output_queue  # 保存队列引用
+        self.streaming_output = True
+        try:
+            # 执行具体的工具逻辑，子类可以重写这个方法
+            result = self.execute(**kwargs)
+            # 如果结果不为空，发送到队列
+            if result:
+                # 对于非流式输出，直接发送完整结果
+                output_queue.put(("complete", result))
+        finally:
+            self.streaming_output = False
+            self.output_queue = None
+    
+    def validate_params(self, required: List[str], **kwargs) -> Optional[str]:
+        """验证必需参数"""
+        missing = [p for p in required if p not in kwargs or kwargs[p] is None]
+        if missing:
+            return f"❌ 错误：缺少必需参数: {', '.join(missing)}"
+        return None
+    
+    def to_function_call_format(self) -> Dict:
+        """转换为OpenAI函数调用格式"""
+        return {
+            "type": "function",
+            "function": {
+                "name": self.get_name(),
+                "description": self.get_description(),
+                "parameters": self.get_parameters()
+            }
+        }
+    
+    def format_result(self, success: bool, message: str, data: Dict = None) -> str:
+        """统一结果格式化"""
+        result = []
+        emoji = "✅" if success else "❌"
+        result.append(f"{emoji} {message}")
+        
+        if data:
+            for key, value in data.items():
+                if isinstance(value, str) and "\n" in value:
+                    result.append(f"  {key}:")
+                    for line in value.split("\n"):
+                        result.append(f"    {line}")
+                else:
+                    result.append(f"  {key}: {value}")
+        
+        return "\n".join(result)
+    
+    def check_path_safety(self, filepath: str) -> Tuple[bool, str, str]:
+        """检查路径安全性（简化版）"""
+        try:
+            abs_path = os.path.abspath(os.path.expanduser(filepath))
+            
+            # 检查文件大小
+            if os.path.exists(abs_path) and os.path.isfile(abs_path):
+                file_size = os.path.getsize(abs_path)
+                max_size = constants.MAX_FILE_SIZE
+                if file_size > max_size:
+                    return False, abs_path, f"❌ 文件太大 ({file_size/1024/1024:.1f}MB)"
+            
+            return True, abs_path, ""
+        except Exception as e:
+            return False, filepath, f"❌ 路径检查错误: {str(e)}"
+    
+    def send_line(self, line: str):
+        """发送一行输出到队列（如果存在）"""
+        if self.output_queue:
+            self.output_queue.put(("line", line))
+    
+    def send_complete(self, result: str):
+        """发送完成结果到队列（如果存在）"""
+        if self.output_queue:
+            self.output_queue.put(("complete", result))