feat(tool_system): implement declarative caching for tools

This commit refactors the tool caching system to be more robust, configurable, and easier to use. The caching logic is centralized within the `wrap_tool_executor`, removing the need for boilerplate code within individual tool implementations.

Key changes:
- Adds `enable_cache`, `cache_ttl`, and `semantic_cache_query_key` attributes to `BaseTool` for declarative cache configuration.
- Moves caching logic from a simple history-based lookup and individual tools into a unified handling process in `wrap_tool_executor`.
- The new system leverages the central `tool_cache` manager for both exact and semantic caching based on tool configuration.
- Refactors `WebSurfingTool` and `URLParserTool` to utilize the new declarative caching mechanism, simplifying their code.

src/plugin_system/base/base_tool.py

@@ -31,6 +31,13 @@ class BaseTool(ABC):
     history_ttl: int = 5
     """工具调用历史记录的TTL值，默认为5。设为0表示不记录历史"""
 
+    enable_cache: bool = False
+    """是否为该工具启用缓存"""
+    cache_ttl: int = 3600
+    """缓存的TTL值（秒），默认为3600秒（1小时）"""
+    semantic_cache_query_key: Optional[str] = None
+    """用于语义缓存的查询参数键名。如果设置，将使用此参数的值进行语义相似度搜索"""
+
     def __init__(self, plugin_config: Optional[dict] = None):
         self.plugin_config = plugin_config or {}  # 直接存储插件配置字典