refactor: unify system tools as resources

backend/services/brains/workflow_brain.py

@@ -57,7 +57,7 @@ from services.message_stage import (
     MessageStageSpec,
 )
 from services.runtime_variables import DynamicVariableError, DynamicVariableStore
-from services.system_tools import SYSTEM_TOOL_KINDS, state_update_properties
+from services.system_tools import state_update_properties, system_tool_kind
 from services.tool_executor import ToolExecutionError, ToolExecutor
 from services.tool_policy import policy_for_tool
 from services.workflow.agent import WorkflowAgentStage
@@ -564,22 +564,21 @@ class WorkflowBrain(BaseBrain):
 
         for tool_id in stage.tool_ids:
             tool = self._tool_by_id.get(str(tool_id))
-            if tool and tool.type in {"http", "mcp", "client"}:
+            if not tool:
+                continue
+            if tool.type == "system":
+                append_function(
+                    self._workflow_system_tool(
+                        tool,
+                        node_id=node_id,
+                        state_variable_names=stage.state_variable_names,
+                    )
+                )
+            elif tool.type in {"http", "mcp", "client"}:
                 append_function(self._flow_tool(tool, node_id))
         append_function(self._knowledge_function(node_id))
         if stage.vision_enabled and self._require_runtime().vision_function:
             append_function(self._require_runtime().vision_function)
-        for kind in stage.system_tools:
-            if kind not in SYSTEM_TOOL_KINDS:
-                logger.warning(f"忽略 Agent {node_id} 的未知系统工具: {kind}")
-                continue
-            append_function(
-                self._workflow_system_tool(
-                    kind,
-                    node_id=node_id,
-                    state_variable_names=stage.state_variable_names,
-                )
-            )
         return self._require_agent_stage().node_config(
             node_id,
             functions=functions,
@@ -738,27 +737,30 @@ class WorkflowBrain(BaseBrain):
 
     def _workflow_system_tool(
         self,
-        kind: str,
+        tool: RuntimeTool,
         *,
         node_id: str,
         state_variable_names: tuple[str, ...],
     ) -> FlowsFunctionSchema:
         """Build one platform-owned tool scoped to the active Agent node."""
+        kind = system_tool_kind(tool.definition or {})
         if kind == "update_state":
             return self._workflow_update_state_tool(
+                tool,
                 node_id,
                 state_variable_names=state_variable_names,
             )
         if kind == "skip_turn":
-            return self._workflow_skip_turn_tool()
+            return self._workflow_skip_turn_tool(tool)
         if kind == "request_human_handoff":
-            return self._workflow_handoff_tool(node_id)
+            return self._workflow_handoff_tool(tool, node_id)
         if kind == "end_conversation":
-            return self._workflow_end_conversation_tool()
-        raise ValueError(f"未知系统工具: {kind}")
+            return self._workflow_end_conversation_tool(tool, node_id)
+        raise ValueError(f"系统工具 {tool.id} 缺少有效 kind")
 
     def _workflow_update_state_tool(
         self,
+        tool: RuntimeTool,
         node_id: str,
         *,
         state_variable_names: tuple[str, ...],
@@ -791,8 +793,8 @@ class WorkflowBrain(BaseBrain):
             }
 
         return FlowsFunctionSchema(
-            name="update_state",
-            description=(
+            name=tool.function_name,
+            description=tool.description or (
                 "静默更新当前阶段明确授权的动态变量。"
                 "只提交本轮获得或确认的信息，更新后继续当前回答。"
             ),
@@ -805,7 +807,7 @@ class WorkflowBrain(BaseBrain):
         )
 
     @staticmethod
-    def _workflow_skip_turn_tool() -> FlowsFunctionSchema:
+    def _workflow_skip_turn_tool(tool: RuntimeTool) -> FlowsFunctionSchema:
         async def handler(args, _flow_manager):
             reason = str((args or {}).get("reason") or "").strip()
             result = {"status": "success", "action": "skip_turn"}
@@ -815,8 +817,8 @@ class WorkflowBrain(BaseBrain):
 
         setattr(handler, "_suppress_followup_llm", True)
         return FlowsFunctionSchema(
-            name="skip_turn",
-            description=(
+            name=tool.function_name,
+            description=tool.description or (
                 "跳过当前轮次，不生成任何语音回复。"
                 "仅当用户明确要求稍等、话还没说完，或输入可确认只是噪音时调用。"
             ),
@@ -830,7 +832,9 @@ class WorkflowBrain(BaseBrain):
             handler=handler,
         )
 
-    def _workflow_handoff_tool(self, node_id: str) -> FlowsFunctionSchema:
+    def _workflow_handoff_tool(
+        self, tool: RuntimeTool, node_id: str
+    ) -> FlowsFunctionSchema:
         async def handler(args, _flow_manager):
             reason = str((args or {}).get("reason") or "human_handoff").strip()
             await self._require_runtime().queue_frame(
@@ -852,8 +856,8 @@ class WorkflowBrain(BaseBrain):
             }
 
         return FlowsFunctionSchema(
-            name="request_human_handoff",
-            description=(
+            name=tool.function_name,
+            description=tool.description or (
                 "提交人工接管请求。当用户明确要求人工服务、投诉升级或 AI 无法"
                 "解决时调用。该工具只提交请求，不代表人工已经接通；调用后继续"
                 "回复用户并说明正在等待人工响应。"
@@ -865,27 +869,55 @@ class WorkflowBrain(BaseBrain):
             handler=handler,
         )
 
-    def _workflow_end_conversation_tool(self) -> FlowsFunctionSchema:
+    def _workflow_end_conversation_tool(
+        self, tool: RuntimeTool, node_id: str
+    ) -> FlowsFunctionSchema:
+        config = (tool.definition or {}).get("config") or {}
+        message_type = str(config.get("message_type") or "none")
+        custom_message = str(config.get("custom_message") or "").strip()
+        capture_reason = bool(config.get("capture_reason", True))
+
         async def handler(args, _flow_manager):
             reason = str((args or {}).get("reason") or "end_conversation").strip()
-            self._waiting_for_generated_end_speech = True
-            self._require_runtime().call_end.begin(reason)
+            uses_custom_message = message_type == "custom" and bool(custom_message)
+            self._waiting_for_generated_end_speech = not uses_custom_message
+            runtime = self._require_runtime()
+            runtime.call_end.begin(reason)
+            if uses_custom_message:
+                await self._queue_visible_speech(
+                    custom_message,
+                    source="workflow-system-tool",
+                    node_id=node_id,
+                )
+                arm_tracked = getattr(
+                    runtime.call_end,
+                    "arm_after_tracked_speech",
+                    None,
+                )
+                if callable(arm_tracked):
+                    await arm_tracked()
+                else:
+                    runtime.call_end.arm_after_speech()
             return {"status": "success", "action": "ending_call"}
 
         setattr(handler, "_suppress_followup_llm", True)
         return FlowsFunctionSchema(
-            name="end_conversation",
-            description=(
+            name=tool.function_name,
+            description=tool.description or (
                 "礼貌地结束本次对话。当用户明确告别、表示任务已完成"
                 "或要求挂断时调用。"
             ),
-            properties={
-                "reason": {
-                    "type": "string",
-                    "description": "结束对话的简短原因。",
+            properties=(
+                {
+                    "reason": {
+                        "type": "string",
+                        "description": "结束对话的简短原因。",
+                    }
                 }
-            },
-            required=[],
+                if capture_reason
+                else {}
+            ),
+            required=["reason"] if capture_reason else [],
             handler=handler,
         )