feat: Introduce unified tooling system for plugins and MCP
- Added a new `tooling` module to define a unified model for tool declarations, invocations, and execution results, facilitating compatibility between plugins, legacy actions, and MCP tools. - Implemented `ToolProvider` interface for various tool providers including built-in tools, MCP tools, and plugin runtime tools. - Enhanced `MCPManager` and `MCPConnection` to support unified tool invocation and execution results. - Updated `ComponentRegistry` and related classes to accommodate the new tool specifications and descriptions. - Refactored existing components to utilize the new tooling system, ensuring backward compatibility with legacy actions. - Improved error handling and logging for tool invocations across different providers.
This commit is contained in:
DrSmoothl
@@ -1,14 +1,21 @@
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"""插件运行时统一组件查询服务。
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该模块统一从插件运行时的 Host ComponentRegistry 中聚合只读视图，
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供 HFC/PFC、Planner、ToolExecutor 和运行时能力层查询与调用。
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供 HFC、ToolExecutor 和运行时能力层查询与调用。
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Awaitable, Callable, Dict, Optional, Tuple
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from typing import TYPE_CHECKING, Any, Awaitable, Callable, Dict, Optional, Tuple, cast
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from src.common.logger import get_logger
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from src.core.tooling import (
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ToolExecutionContext,
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ToolExecutionResult,
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ToolInvocation,
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ToolSpec,
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build_tool_detailed_description,
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)
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from src.core.types import ActionActivationType, ActionInfo, CommandInfo, ComponentInfo, ComponentType, ToolInfo
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from src.llm_models.payload_content.tool_option import normalize_tool_option
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@@ -240,12 +247,38 @@ class ComponentQueryService:
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return ToolInfo(
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name=entry.name,
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description=entry.description,
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description=entry.brief_description or entry.description,
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enabled=bool(entry.enabled),
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plugin_name=entry.plugin_id,
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parameters_schema=ComponentQueryService._build_tool_parameters_schema(entry),
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)
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@staticmethod
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def _build_tool_spec(entry: "ToolEntry") -> ToolSpec:
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"""将运行时 Tool 条目转换为统一工具声明。
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Args:
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entry: 插件运行时中的 Tool 条目。
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Returns:
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ToolSpec: 统一工具声明。
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"""
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parameters_schema = ComponentQueryService._build_tool_parameters_schema(entry)
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return ToolSpec(
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name=entry.name,
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brief_description=entry.brief_description or entry.description or f"工具 {entry.name}",
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detailed_description=entry.detailed_description or build_tool_detailed_description(parameters_schema),
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parameters_schema=parameters_schema,
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provider_name=entry.plugin_id,
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provider_type="plugin",
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metadata={
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"plugin_id": entry.plugin_id,
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"invoke_method": entry.invoke_method,
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"legacy_component_type": entry.legacy_component_type,
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},
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)
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@staticmethod
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def _log_duplicate_component(component_type: ComponentType, component_name: str) -> None:
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"""记录重复组件名称冲突。
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@@ -475,7 +508,12 @@ class ComponentQueryService:
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return _executor
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@staticmethod
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def _build_tool_executor(supervisor: "PluginSupervisor", plugin_id: str, component_name: str) -> ToolExecutor:
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def _build_tool_executor(
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supervisor: "PluginSupervisor",
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plugin_id: str,
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component_name: str,
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invoke_method: str = "plugin.invoke_tool",
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) -> ToolExecutor:
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"""构造工具执行 RPC 闭包。
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Args:
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@@ -499,7 +537,7 @@ class ComponentQueryService:
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try:
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response = await supervisor.invoke_plugin(
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method="plugin.invoke_tool",
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method=invoke_method,
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plugin_id=plugin_id,
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component_name=component_name,
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args=function_args,
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@@ -615,7 +653,162 @@ class ComponentQueryService:
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if matched_entry is None:
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return None
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supervisor, entry = matched_entry
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return self._build_tool_executor(supervisor, entry.plugin_id, entry.name)
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tool_entry = cast("ToolEntry", entry)
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return self._build_tool_executor(supervisor, tool_entry.plugin_id, tool_entry.name, tool_entry.invoke_method)
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def get_llm_available_tool_specs(self) -> Dict[str, ToolSpec]:
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"""获取当前可供 LLM 使用的统一工具声明集合。
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Returns:
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Dict[str, ToolSpec]: 工具名到工具声明的映射。
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"""
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collected_specs: Dict[str, ToolSpec] = {}
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for _supervisor, entry in self._iter_component_entries(ComponentType.TOOL):
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if entry.name in collected_specs:
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self._log_duplicate_component(ComponentType.TOOL, entry.name)
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continue
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collected_specs[entry.name] = self._build_tool_spec(entry) # type: ignore[arg-type]
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return collected_specs
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@staticmethod
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def _build_tool_invocation_payload(
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entry: "ToolEntry",
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invocation: ToolInvocation,
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context: Optional[ToolExecutionContext],
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) -> Dict[str, Any]:
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"""构造插件工具执行时发送给 Runner 的参数。
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Args:
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entry: 目标工具条目。
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invocation: 统一工具调用请求。
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context: 统一工具执行上下文。
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Returns:
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Dict[str, Any]: 发往 Runner 的参数字典。
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"""
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payload = dict(invocation.arguments)
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if entry.invoke_method == "plugin.invoke_action":
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stream_id = context.stream_id if context is not None else invocation.stream_id
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reasoning = context.reasoning if context is not None else invocation.reasoning
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payload = {
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**payload,
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"stream_id": stream_id,
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"chat_id": stream_id,
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"reasoning": reasoning,
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"action_data": dict(invocation.arguments),
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}
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return payload
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@staticmethod
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def _parse_tool_invoke_result(
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entry: "ToolEntry",
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result: Any,
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) -> ToolExecutionResult:
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"""将插件组件返回值转换为统一工具执行结果。
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Args:
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entry: 目标工具条目。
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result: 插件组件原始返回值。
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Returns:
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ToolExecutionResult: 统一执行结果。
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"""
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if isinstance(result, dict):
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success = bool(result.get("success", True))
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content = str(result.get("content", result.get("message", "")) or "").strip()
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error_message = ""
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if not success:
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error_message = str(result.get("error", result.get("message", "插件工具执行失败")) or "").strip()
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return ToolExecutionResult(
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tool_name=entry.name,
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success=success,
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content=content,
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error_message=error_message,
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structured_content=result,
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metadata={"plugin_id": entry.plugin_id},
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)
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if isinstance(result, (list, tuple)) and result:
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if isinstance(result[0], bool):
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success = bool(result[0])
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message = "" if len(result) < 2 or result[1] is None else str(result[1]).strip()
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return ToolExecutionResult(
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tool_name=entry.name,
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success=success,
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content=message if success else "",
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error_message="" if success else message,
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structured_content=list(result),
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metadata={"plugin_id": entry.plugin_id},
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)
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normalized_content = "" if result is None else str(result).strip()
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return ToolExecutionResult(
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tool_name=entry.name,
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success=True,
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content=normalized_content,
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structured_content=result,
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metadata={"plugin_id": entry.plugin_id},
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)
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async def invoke_tool_as_tool(
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self,
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invocation: ToolInvocation,
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context: Optional[ToolExecutionContext] = None,
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) -> ToolExecutionResult:
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"""按统一工具语义执行插件工具。
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Args:
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invocation: 统一工具调用请求。
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context: 执行上下文。
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Returns:
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ToolExecutionResult: 统一工具执行结果。
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"""
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matched_entry = self._get_unique_component_entry(ComponentType.TOOL, invocation.tool_name)
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if matched_entry is None:
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return ToolExecutionResult(
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tool_name=invocation.tool_name,
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success=False,
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error_message=f"未找到插件工具：{invocation.tool_name}",
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)
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supervisor, entry = matched_entry
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tool_entry = cast("ToolEntry", entry)
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invoke_payload = self._build_tool_invocation_payload(tool_entry, invocation, context)
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try:
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response = await supervisor.invoke_plugin(
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method=tool_entry.invoke_method,
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plugin_id=tool_entry.plugin_id,
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component_name=tool_entry.name,
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args=invoke_payload,
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timeout_ms=30000,
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)
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except Exception as exc:
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logger.error(f"运行时工具 {tool_entry.plugin_id}.{tool_entry.name} 执行失败: {exc}", exc_info=True)
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return ToolExecutionResult(
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tool_name=tool_entry.name,
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success=False,
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error_message=str(exc),
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metadata={"plugin_id": tool_entry.plugin_id},
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)
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payload = response.payload if isinstance(response.payload, dict) else {}
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transport_success = bool(payload.get("success", False))
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result = payload.get("result")
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if not transport_success:
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return ToolExecutionResult(
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tool_name=tool_entry.name,
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success=False,
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error_message="" if result is None else str(result),
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structured_content=result,
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metadata={"plugin_id": tool_entry.plugin_id},
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)
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return self._parse_tool_invoke_result(tool_entry, result)
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def get_llm_available_tools(self) -> Dict[str, ToolInfo]:
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"""获取当前可供 LLM 选择的工具集合。
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