Merge branch 'dev' of https://github.com/MoFox-Studio/MoFox_Bot into dev

src/plugin_system/apis/tool_api.py

@@ -1,3 +1,4 @@
+from typing import Any
 from src.common.logger import get_logger
 from src.plugin_system.base.base_tool import BaseTool
 from src.plugin_system.base.component_types import ComponentType
@@ -20,13 +21,22 @@ def get_tool_instance(tool_name: str) -> BaseTool | None:
     return tool_class(plugin_config) if tool_class else None
 
 
-def get_llm_available_tool_definitions():
+def get_llm_available_tool_definitions() -> list[dict[str, Any]]:
     """获取LLM可用的工具定义列表
 
     Returns:
-        List[Tuple[str, Dict[str, Any]]]: 工具定义列表，为[("tool_name", 定义)]
+        list[dict[str, Any]]: 工具定义列表
     """
     from src.plugin_system.core import component_registry
 
     llm_available_tools = component_registry.get_llm_available_tools()
+    tool_definitions = []
+    for tool_name, tool_class in llm_available_tools.items():
+        try:
+            # 调用类方法 get_tool_definition 获取定义
+            definition = tool_class.get_tool_definition()
+            tool_definitions.append(definition)
+        except Exception as e:
+            logger.error(f"获取工具 {tool_name} 的定义失败: {e}")
+    return tool_definitions
 

src/plugin_system/core/tool_use.py

@@ -113,10 +113,14 @@ class ToolExecutor:
         logger.debug(f"{self.log_prefix}开始LLM工具调用分析")
 
         # 调用LLM进行工具决策
-        response, (reasoning_content, model_name, tool_calls) = await self.llm_model.generate_response_async(
+        response, llm_extra_info = await self.llm_model.generate_response_async(
             prompt=prompt, tools=tools, raise_when_empty=False
         )
 
+        tool_calls = None
+        if llm_extra_info and isinstance(llm_extra_info, tuple) and len(llm_extra_info) == 3:
+            _, _, tool_calls = llm_extra_info
+
         # 执行工具调用
         tool_results, used_tools = await self.execute_tool_calls(tool_calls)
 
@@ -133,7 +137,9 @@ class ToolExecutor:
         user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
 
         # 获取基础工具定义（包括二步工具的第一步）
-        tool_definitions = [definition for name, definition in all_tools if name not in user_disabled_tools]
+        tool_definitions = [
+            definition for definition in all_tools if definition.get("function", {}).get("name") not in user_disabled_tools
+        ]
 
         # 检查是否有待处理的二步工具第二步调用
         pending_step_two = getattr(self, "_pending_step_two_tools", {})
@@ -282,20 +288,7 @@ class ToolExecutor:
             )
             
             # 检查是否是MCP工具
-            try:
-                from src.plugin_system.utils.mcp_tool_provider import mcp_tool_provider
-                if function_name in mcp_tool_provider.mcp_tools:
-                    logger.info(f"{self.log_prefix}执行MCP工具: {function_name}")
-                    result = await mcp_tool_provider.call_mcp_tool(function_name, function_args)
-                    return {
-                        "tool_call_id": tool_call.call_id,
-                        "role": "tool",
-                        "name": function_name,
-                        "type": "function",
-                        "content": result.get("content", ""),
-                    }
-            except Exception as e:
-                logger.debug(f"检查MCP工具时出错: {e}")
+            pass
             
             function_args["llm_called"] = True  # 标记为LLM调用