update:1.更新mcp插件功能，目前只支持remote调用
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xiamuceer
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"""MCP工具服务 - 统一管理MCP工具的注入和执行"""
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from typing import List, Dict, Any, Optional
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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import asyncio
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import json
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from datetime import datetime
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from app.models.mcp_plugin import MCPPlugin
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from app.mcp.registry import mcp_registry
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from app.logger import get_logger
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logger = get_logger(__name__)
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class MCPToolServiceError(Exception):
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"""MCP工具服务异常"""
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pass
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class MCPToolService:
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"""MCP工具服务 - 统一管理MCP工具的注入和执行"""
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def __init__(self):
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self._tool_cache = {} # 工具定义缓存
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self._result_cache = {} # 工具结果缓存（可选）
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async def get_user_enabled_tools(
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self,
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user_id: str,
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db_session: AsyncSession,
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category: Optional[str] = None
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) -> List[Dict[str, Any]]:
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"""
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获取用户启用的MCP工具列表
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Args:
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user_id: 用户ID
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db_session: 数据库会话
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category: 工具类别筛选（search/analysis/filesystem等）
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Returns:
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工具定义列表，格式符合OpenAI Function Calling规范
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"""
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try:
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# 1. 查询用户启用的插件（enabled=True即可，不强制要求status=active）
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# 因为新启用的插件status可能还是inactive，需要给它机会被调用
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query = select(MCPPlugin).where(
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MCPPlugin.user_id == user_id,
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MCPPlugin.enabled == True
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)
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if category:
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query = query.where(MCPPlugin.category == category)
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result = await db_session.execute(query)
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plugins = result.scalars().all()
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if not plugins:
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logger.info(f"用户 {user_id} 没有启用的MCP插件")
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return []
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# 2. 获取所有工具定义
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all_tools = []
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for plugin in plugins:
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try:
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# 确保插件已加载到注册表
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if not mcp_registry.get_client(user_id, plugin.plugin_name):
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logger.info(f"插件 {plugin.plugin_name} 未加载，尝试加载...")
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success = await mcp_registry.load_plugin(plugin)
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if not success:
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logger.warning(f"插件 {plugin.plugin_name} 加载失败，跳过")
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continue
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# 从registry获取该插件的工具列表
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plugin_tools = await mcp_registry.get_plugin_tools(
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user_id=user_id,
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plugin_name=plugin.plugin_name
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)
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# 格式化为Function Calling格式
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formatted_tools = self._format_tools_for_ai(
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plugin_tools,
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plugin.plugin_name # ✅ 修复：使用正确的属性名plugin_name
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)
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all_tools.extend(formatted_tools)
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logger.info(
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f"从插件 {plugin.plugin_name} 加载了 "
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f"{len(formatted_tools)} 个工具"
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)
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except Exception as e:
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logger.error(
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f"获取插件 {plugin.plugin_name} 的工具失败: {e}",
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exc_info=True
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)
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continue
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logger.info(f"用户 {user_id} 共加载 {len(all_tools)} 个MCP工具")
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return all_tools
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except Exception as e:
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logger.error(f"获取用户MCP工具失败: {e}", exc_info=True)
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raise MCPToolServiceError(f"获取MCP工具失败: {str(e)}")
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def _format_tools_for_ai(
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self,
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plugin_tools: List[Dict[str, Any]],
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plugin_name: str
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) -> List[Dict[str, Any]]:
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"""
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将MCP工具定义格式化为AI Function Calling格式
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Args:
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plugin_tools: MCP插件的工具列表
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plugin_name: 插件名称
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Returns:
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格式化后的工具列表
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"""
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formatted_tools = []
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for tool in plugin_tools:
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formatted_tool = {
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"type": "function",
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"function": {
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"name": f"{plugin_name}_{tool['name']}", # 加插件前缀避免冲突
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"description": tool.get("description", ""),
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"parameters": tool.get("inputSchema", {
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"type": "object",
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"properties": {},
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"required": []
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})
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}
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}
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formatted_tools.append(formatted_tool)
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return formatted_tools
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async def execute_tool_calls(
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self,
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user_id: str,
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tool_calls: List[Dict[str, Any]],
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db_session: AsyncSession,
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timeout: float = 60.0
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) -> List[Dict[str, Any]]:
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"""
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批量执行AI请求的工具调用（并行执行）
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Args:
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user_id: 用户ID
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tool_calls: AI返回的工具调用列表
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db_session: 数据库会话
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timeout: 单个工具调用的超时时间（秒，默认30秒）
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Returns:
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工具调用结果列表
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"""
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if not tool_calls:
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return []
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logger.info(f"开始执行 {len(tool_calls)} 个工具调用")
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# 创建异步任务列表
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tasks = [
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self._execute_single_tool(
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user_id=user_id,
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tool_call=tool_call,
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db_session=db_session,
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timeout=timeout
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)
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for tool_call in tool_calls
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]
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# 并行执行所有工具调用
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results = await asyncio.gather(*tasks, return_exceptions=True)
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# 处理结果
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formatted_results = []
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for i, result in enumerate(results):
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tool_call = tool_calls[i]
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if isinstance(result, Exception):
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# 工具调用异常
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formatted_results.append({
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"tool_call_id": tool_call.get("id", f"call_{i}"),
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"role": "tool",
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"name": tool_call["function"]["name"],
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"content": f"工具调用失败: {str(result)}",
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"success": False,
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"error": str(result)
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})
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else:
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formatted_results.append(result)
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return formatted_results
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async def _execute_single_tool(
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self,
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user_id: str,
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tool_call: Dict[str, Any],
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db_session: AsyncSession,
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timeout: float
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) -> Dict[str, Any]:
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"""
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执行单个工具调用
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Args:
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user_id: 用户ID
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tool_call: 工具调用信息
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db_session: 数据库会话
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timeout: 超时时间
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Returns:
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工具调用结果
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"""
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tool_call_id = tool_call.get("id", "unknown")
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function_name = tool_call["function"]["name"]
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try:
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# 解析插件名和工具名
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if "_" in function_name:
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plugin_name, tool_name = function_name.split("_", 1)
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else:
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raise ValueError(f"无效的工具名称格式: {function_name}")
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# 解析参数
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arguments_str = tool_call["function"]["arguments"]
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if isinstance(arguments_str, str):
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arguments = json.loads(arguments_str)
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else:
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arguments = arguments_str
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logger.info(
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f"执行工具: {plugin_name}.{tool_name}, "
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f"参数: {arguments}"
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)
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# 设置超时
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try:
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result = await asyncio.wait_for(
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mcp_registry.call_tool(
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user_id=user_id,
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plugin_name=plugin_name,
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tool_name=tool_name,
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arguments=arguments
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),
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timeout=timeout
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)
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# 成功返回
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return {
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"tool_call_id": tool_call_id,
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"role": "tool",
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"name": function_name,
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"content": json.dumps(result, ensure_ascii=False),
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"success": True,
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"error": None
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}
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except asyncio.TimeoutError:
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raise MCPToolServiceError(
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f"工具调用超时（>{timeout}秒）"
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)
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except Exception as e:
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logger.error(
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f"工具 {function_name} 调用失败: {e}",
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exc_info=True
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)
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return {
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"tool_call_id": tool_call_id,
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"role": "tool",
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"name": function_name,
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"content": f"工具调用失败: {str(e)}",
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"success": False,
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"error": str(e)
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}
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async def build_tool_context(
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self,
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tool_results: List[Dict[str, Any]],
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format: str = "markdown"
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) -> str:
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"""
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将工具调用结果格式化为上下文文本
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Args:
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tool_results: 工具调用结果列表
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format: 输出格式（markdown/json/plain）
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Returns:
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格式化的上下文字符串
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"""
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if not tool_results:
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return ""
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if format == "markdown":
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return self._build_markdown_context(tool_results)
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elif format == "json":
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return json.dumps(tool_results, ensure_ascii=False, indent=2)
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else: # plain
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return self._build_plain_context(tool_results)
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def _build_markdown_context(
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self,
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tool_results: List[Dict[str, Any]]
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) -> str:
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"""构建Markdown格式的工具上下文"""
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lines = ["## 🔧 工具调用结果\n"]
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for i, result in enumerate(tool_results, 1):
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tool_name = result.get("name", "unknown")
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success = result.get("success", False)
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content = result.get("content", "")
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status_emoji = "✅" if success else "❌"
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lines.append(f"### {status_emoji} {i}. {tool_name}\n")
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if success:
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# 尝试美化JSON内容
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try:
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content_obj = json.loads(content)
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content = json.dumps(content_obj, ensure_ascii=False, indent=2)
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except:
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pass
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lines.append(f"```json\n{content}\n```\n")
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else:
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lines.append(f"**错误**: {content}\n")
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return "\n".join(lines)
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def _build_plain_context(
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self,
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tool_results: List[Dict[str, Any]]
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) -> str:
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"""构建纯文本格式的工具上下文"""
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lines = ["=== 工具调用结果 ===\n"]
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for i, result in enumerate(tool_results, 1):
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tool_name = result.get("name", "unknown")
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success = result.get("success", False)
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content = result.get("content", "")
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status = "成功" if success else "失败"
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lines.append(f"{i}. {tool_name} - {status}")
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lines.append(f" 结果: {content}\n")
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return "\n".join(lines)
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# 全局单例
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mcp_tool_service = MCPToolService()
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