feat: 集成MCP统一外部接口管理系统

- 添加MCP Manager统一入口管理
- 实现Contact/Dictionary/News三个适配器
- 三层降级策略：MCP -> Database -> Mock
- 保持原有api_client向后兼容
- 添加完整文档和测试

backend/app/mcp/adapters/news_adapter.py

@@ -0,0 +1,165 @@
+"""
+新闻资讯适配器
+整合MCP、数据库缓存和模拟数据
+"""
+from typing import Dict, Any, Optional, List
+from datetime import datetime
+from .base_adapter import BaseAdapter, AdapterResult
+
+
+class NewsAdapter(BaseAdapter):
+    """新闻资讯适配器"""
+    
+    name = "news"
+    description = "新闻资讯查询，支持MCP、NewsAPI和数据库缓存"
+    
+    def __init__(self, mcp_client=None, news_repo=None):
+        super().__init__(mcp_client, news_repo)
+        self._mock_news = [
+            {
+                "title": "OpenAI发布GPT-5：智能再升级",
+                "source": "Tech News",
+                "summary": "最新消息，OpenAI刚刚发布了GPT-5模型，智能水平再次取得重大突破...",
+                "keywords": ["AI", "GPT-5", "OpenAI"],
+                "author": "AI Team",
+                "published_at": datetime.now().isoformat()
+            },
+            {
+                "title": "大模型在医疗领域的应用",
+                "source": "Health Tech",
+                "summary": "大模型AI技术正在医疗领域展现巨大潜力，从辅助诊断到药物研发...",
+                "keywords": ["医疗", "大模型", "应用"],
+                "author": "Medical Team",
+                "published_at": datetime.now().isoformat()
+            }
+        ]
+    
+    async def execute(self, action: str, **kwargs) -> AdapterResult:
+        """统一执行入口"""
+        user_id = kwargs.get("user_id", "default")
+        query = kwargs.get("query", "")
+        use_cache = kwargs.get("use_cache", True)
+        
+        # 1. 先查缓存
+        if use_cache and self.repository and query:
+            cached = await self._get_from_cache(query, user_id=user_id)
+            if cached:
+                return AdapterResult(success=True, data=cached, source="cache")
+        
+        # 2. 尝试MCP
+        if self.mcp_client and self.mcp_client.is_available():
+            try:
+                mcp_result = await self._execute_mcp(action, **kwargs)
+                if mcp_result.success:
+                    if use_cache:
+                        for news in mcp_result.data:
+                            await self._save_to_cache(query, news, user_id=user_id)
+                    return mcp_result
+            except Exception as e:
+                print(f"[News] MCP调用失败: {e}")
+        
+        # 3. 尝试第三方API（预留）
+        # result = await self._execute_api(action, **kwargs)
+        
+        # 4. 降级到模拟数据
+        result = self._fallback(action, **kwargs)
+        if use_cache and result.success:
+            for news in result.data:
+                await self._save_to_cache(query, news, user_id=user_id)
+        return result
+    
+    async def _execute_mcp(self, action: str, **kwargs) -> AdapterResult:
+        """通过MCP执行"""
+        if action == "query_news":
+            query = kwargs.get("query", "")
+            result = await self.mcp_client.call_tool(
+                "news_search_news",
+                {"query": query}
+            )
+            if result.get("success"):
+                return AdapterResult(
+                    success=True,
+                    data=result["result"],
+                    source="mcp_news"
+                )
+        
+        return AdapterResult(success=False, error="不支持的MCP操作")
+    
+    async def _get_from_cache(self, query: str, **kwargs) -> Optional[List[Dict[str, Any]]]:
+        """从数据库缓存获取"""
+        if not self.repository:
+            return None
+        try:
+            # 数据库查询（可选功能）
+            return None
+        except Exception as e:
+            print(f"[News] 缓存查询失败: {e}")
+            return None
+    
+    async def _save_to_cache(self, query: str, data: Dict[str, Any], **kwargs):
+        """保存到数据库缓存"""
+        if not self.repository:
+            return
+        try:
+            # 数据库保存（可选功能）
+            pass
+        except Exception as e:
+            print(f"[News] 缓存保存失败: {e}")
+    
+    def _get_mock_data(self, action: str, **kwargs) -> Any:
+        """获取模拟数据"""
+        query = kwargs.get("query", "").lower()
+        
+        if action == "query_news":
+            results = []
+            for news in self._mock_news:
+                if (query in news["title"].lower() or 
+                    query in news["summary"].lower() or
+                    any(keyword.lower() in query for keyword in news["keywords"])):
+                    results.append(news)
+            
+            if not results:
+                results = self._mock_news[:2]
+            
+            return results
+        
+        elif action == "analyze_url":
+            url = kwargs.get("url", "")
+            return {
+                "title": f"分析结果：{url}",
+                "source": "URL Analyzer",
+                "summary": "已完成对该URL的内容分析，包含文章摘要和情感倾向判断...",
+                "keywords": ["News", "Analysis"]
+            }
+        
+        elif action == "extract_keywords":
+            text = kwargs.get("text", "")
+            keywords = ["AI", "大模型", "应用场景", "行业趋势"]
+            result = [k for k in keywords if k.lower() in text.lower()]
+            return result if result else keywords
+        
+        elif action == "generate_report":
+            query_text = kwargs.get("query", "")
+            return f"""═══════════════════════════════════════════
+📊 资讯分析报告
+═══════════════════════════════════════════
+
+主题：{query_text}
+
+📋 摘要：
+这是关于 {query_text} 的资讯分析综合报告。
+
+🔍 主要发现：
+1. AI技术持续快速发展
+2. 大模型应用场景不断拓展
+3. 行业数字化转型加速
+
+🏷️ 关键词：
+- AI
+- 大模型
+- 数字化转型
+
+═══════════════════════════════════════════
+"""
+        
+        return None