设计一个企业知识库 MCP Server

┌─────────────────────────────────────┐
│         MCP Client (Claude)         │
└───────────────┬─────────────────────┘
                │ SSE/HTTP
┌───────────────▼─────────────────────┐
│       企业知识库 MCP Server          │
├───────────────┬─────────────────────┤
│  API Gateway  │  Auth Middleware    │
│  Tool Router  │  Rate Limiter       │
│  Cache Layer  │  Audit Logger       │
└───────────────┴─────────────────────┘
                │
┌───────────────┬─────────────────────┐
│  Search Engine │  Vector Database   │
│  (Elasticsearch)│  (Pinecone/Qdrant) │
└───────────────┴─────────────────────┘
                │
┌───────────────┬─────────────────────┐
│ Document Store│  External Systems  │
│  (S3/MinIO)   │  (Confluence/Git)   │
└───────────────┴─────────────────────┘

二、工具（Tools）设计

1. 文档操作工具

// 完整工具集定义
{
  "tools": [
    {
      "name": "knowledge_search",
      "description": "搜索企业知识库文档",
      "inputSchema": {
        "type": "object",
        "properties": {
          "query": {
            "type": "string",
            "description": "搜索关键词"
          },
          "filters": {
            "type": "object",
            "description": "过滤条件",
            "properties": {
              "department": {
                "type": "string",
                "enum": ["engineering", "sales", "hr", "finance"]
              },
              "doc_type": {
                "type": "string",
                "enum": ["policy", "guide", "api", "meeting"]
              },
              "author": { "type": "string" },
              "date_range": {
                "type": "object",
                "properties": {
                  "from": { "type": "string", "format": "date" },
                  "to": { "type": "string", "format": "date" }
                }
              },
              "security_level": {
                "type": "string",
                "enum": ["public", "internal", "confidential"]
              }
            }
          },
          "limit": {
            "type": "integer",
            "minimum": 1,
            "maximum": 50,
            "default": 10
          },
          "page": {
            "type": "integer",
            "minimum": 1,
            "default": 1
          },
          "sort_by": {
            "type": "string",
            "enum": ["relevance", "date_desc", "date_asc", "views"],
            "default": "relevance"
          }
        },
        "required": ["query"]
      }
    },
    {
      "name": "knowledge_upload",
      "description": "上传文档到知识库",
      "inputSchema": {
        "type": "object",
        "properties": {
          "title": { "type": "string" },
          "content": { "type": "string" },
          "file_content": {
            "type": "string",
            "description": "Base64编码的文件内容"
          },
          "file_type": {
            "type": "string",
            "enum": ["text", "markdown", "pdf", "docx", "html"]
          },
          "metadata": {
            "type": "object",
            "properties": {
              "department": { "type": "string" },
              "tags": { "type": "array", "items": { "type": "string" } },
              "security_level": { "type": "string" },
              "expires_at": { "type": "string", "format": "date" }
            }
          }
        },
        "required": ["title"]
      }
    },
    {
      "name": "get_document",
      "description": "获取特定文档内容",
      "inputSchema": {
        "type": "object",
        "properties": {
          "doc_id": { "type": "string" },
          "include_metadata": { "type": "boolean", "default": true },
          "include_embeddings": { "type": "boolean", "default": false }
        },
        "required": ["doc_id"]
      }
    },
    {
      "name": "update_document",
      "description": "更新文档",
      "inputSchema": {
        "type": "object",
        "properties": {
          "doc_id": { "type": "string" },
          "content": { "type": "string" },
          "metadata": { "type": "object" },
          "update_reason": { "type": "string" }
        },
        "required": ["doc_id"]
      }
    },
    {
      "name": "delete_document",
      "description": "删除文档（需要确认）",
      "inputSchema": {
        "type": "object",
        "properties": {
          "doc_id": { "type": "string" },
          "reason": { "type": "string" }
        },
        "required": ["doc_id", "reason"]
      }
    }
  ]
}

2. 智能处理工具

{
  "tools": [
    {
      "name": "summarize_document",
      "description": "生成文档摘要",
      "inputSchema": {
        "type": "object",
        "properties": {
          "doc_id": { "type": "string" },
          "length": {
            "type": "string",
            "enum": ["short", "medium", "detailed"],
            "default": "medium"
          },
          "language": { "type": "string", "default": "zh" }
        },
        "required": ["doc_id"]
      }
    },
    {
      "name": "translate_document",
      "description": "翻译文档",
      "inputSchema": {
        "type": "object",
        "properties": {
          "doc_id": { "type": "string" },
          "target_language": { "type": "string" },
          "include_original": { "type": "boolean", "default": false }
        },
        "required": ["doc_id", "target_language"]
      }
    },
    {
      "name": "find_similar",
      "description": "查找相似文档",
      "inputSchema": {
        "type": "object",
        "properties": {
          "doc_id": { "type": "string" },
          "query": { "type": "string" },
          "top_k": { "type": "integer", "default": 5 },
          "similarity_threshold": {
            "type": "number",
            "minimum": 0,
            "maximum": 1,
            "default": 0.7
          }
        }
      }
    },
    {
      "name": "ask_question",
      "description": "基于文档内容问答",
      "inputSchema": {
        "type": "object",
        "properties": {
          "question": { "type": "string" },
          "doc_ids": {
            "type": "array",
            "items": { "type": "string" },
            "description": "指定在哪些文档中搜索"
          },
          "scope": {
            "type": "string",
            "enum": ["all", "department", "personal"],
            "default": "all"
          },
          "include_sources": { "type": "boolean", "default": true }
        },
        "required": ["question"]
      }
    }
  ]
}

3. 管理与集成工具

{
  "tools": [
    {
      "name": "sync_external",
      "description": "同步外部系统文档",
      "inputSchema": {
        "type": "object",
        "properties": {
          "source": {
            "type": "string",
            "enum": ["confluence", "github", "sharepoint", "notion"]
          },
          "config": { "type": "object" },
          "full_sync": { "type": "boolean", "default": false }
        },
        "required": ["source"]
      }
    },
    {
      "name": "generate_report",
      "description": "生成知识库使用报告",
      "inputSchema": {
        "type": "object",
        "properties": {
          "report_type": {
            "type": "string",
            "enum": ["usage", "coverage", "freshness", "popularity"]
          },
          "time_range": {
            "type": "object",
            "properties": {
              "start": { "type": "string", "format": "date" },
              "end": { "type": "string", "format": "date" }
            }
          },
          "format": {
            "type": "string",
            "enum": ["markdown", "json", "html"],
            "default": "markdown"
          }
        },
        "required": ["report_type"]
      }
    },
    {
      "name": "manage_permissions",
      "description": "管理文档权限",
      "inputSchema": {
        "type": "object",
        "properties": {
          "action": {
            "type": "string",
            "enum": ["grant", "revoke", "list"]
          },
          "doc_id": { "type": "string" },
          "user_or_group": { "type": "string" },
          "permission": {
            "type": "string",
            "enum": ["read", "write", "admin"]
          }
        },
        "required": ["action"]
      }
    }
  ]
}

三、资源（Resources）设计

{
  "resources": [
    {
      "uri": "knowledge://recent/{limit?}",
      "name": "最近更新文档",
      "description": "最近更新的知识库文档",
      "mimeType": "application/json"
    },
    {
      "uri": "knowledge://popular/{limit?}",
      "name": "热门文档",
      "description": "查看量最高的文档",
      "mimeType": "application/json"
    },
    {
      "uri": "knowledge://stats/overview",
      "name": "知识库统计概览",
      "description": "知识库使用统计信息",
      "mimeType": "application/json"
    },
    {
      "uri": "knowledge://category/{category}",
      "name": "分类文档",
      "description": "按分类浏览文档",
      "mimeType": "application/json"
    },
    {
      "uri": "knowledge://user/{user_id}/recent",
      "name": "用户最近访问",
      "description": "用户最近访问的文档",
      "mimeType": "application/json"
    }
  ]
}

四、完整实现示例（Python）

import asyncio
import json
import base64
from typing import Dict, Any, List, Optional
from datetime import datetime
from enum import Enum
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from pydantic import BaseModel, Field
import aiohttp
from elasticsearch import AsyncElasticsearch
from qdrant_client import QdrantClient
import hashlib
import uuid

# ========== 数据模型 ==========
class DocumentMetadata(BaseModel):
    department: str
    doc_type: str = Field(default="document")
    security_level: str = Field(default="internal")
    tags: List[str] = Field(default_factory=list)
    author: str
    created_at: datetime = Field(default_factory=datetime.now)
    updated_at: datetime = Field(default_factory=datetime.now)
    expires_at: Optional[datetime] = None
    views: int = 0
    permissions: Dict[str, List[str]] = Field(default_factory=dict)

class Document(BaseModel):
    id: str = Field(default_factory=lambda: str(uuid.uuid4()))
    title: str
    content: str
    summary: Optional[str] = None
    metadata: DocumentMetadata
    embeddings: Optional[List[float]] = None
    file_hash: str  # 用于去重

class SearchFilters(BaseModel):
    department: Optional[str] = None
    doc_type: Optional[str] = None
    security_level: Optional[str] = None
    author: Optional[str] = None
    tags: Optional[List[str]] = None
    date_range: Optional[Dict[str, datetime]] = None

# ========== 权限控制 ==========
class PermissionManager:
    ROLES = {
        "admin": ["read", "write", "delete", "manage"],
        "editor": ["read", "write"],
        "viewer": ["read"],
        "guest": ["read_public"]
    }
    
    def __init__(self):
        self.user_roles: Dict[str, str] = {}
    
    def check_permission(self, user: str, doc: Document, action: str) -> bool:
        """检查用户对文档的权限"""
        role = self.user_roles.get(user, "guest")
        
        # 检查角色权限
        if action not in self.ROLES.get(role, []):
            return False
        
        # 检查文档级别的权限
        if doc.metadata.security_level == "confidential":
            return role in ["admin", "editor"]
        
        # 检查自定义权限
        if user in doc.metadata.permissions.get(action, []):
            return True
            
        return role in ["admin", "editor"]

# ========== 知识库 MCP Server ==========
class KnowledgeBaseMCPServer:
    def __init__(self):
        # 存储
        self.documents: Dict[str, Document] = {}
        self.permission_mgr = PermissionManager()
        
        # 搜索和向量数据库
        self.es = AsyncElasticsearch(["localhost:9200"])
        self.qdrant = QdrantClient("localhost", port=6333)
        
        # 缓存
        self.cache = {}
        
        # 审计日志
        self.audit_log = []
    
    # ========== 工具实现 ==========
    
    async def handle_knowledge_search(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """搜索文档实现"""
        try:
            query = params.get("query", "")
            filters = params.get("filters", {})
            limit = params.get("limit", 10)
            page = params.get("page", 1)
            
            # 构建 ES 查询
            es_query = {
                "query": {
                    "bool": {
                        "must": [{
                            "multi_match": {
                                "query": query,
                                "fields": ["title^3", "content", "summary^2"],
                                "type": "best_fields"
                            }
                        }],
                        "filter": []
                    }
                },
                "from": (page - 1) * limit,
                "size": limit
            }
            
            # 添加过滤器
            if filters:
                filter_clauses = []
                if department := filters.get("department"):
                    filter_clauses.append({"term": {"department": department}})
                if doc_type := filters.get("doc_type"):
                    filter_clauses.append({"term": {"doc_type": doc_type}})
                if security_level := filters.get("security_level"):
                    filter_clauses.append({"term": {"security_level": security_level}})
                if date_range := filters.get("date_range"):
                    filter_clauses.append({
                        "range": {
                            "created_at": {
                                "gte": date_range.get("from"),
                                "lte": date_range.get("to")
                            }
                        }
                    })
                
                if filter_clauses:
                    es_query["query"]["bool"]["filter"] = filter_clauses
            
            # 执行搜索
            response = await self.es.search(
                index="knowledge_docs",
                body=es_query
            )
            
            # 处理结果
            results = []
            for hit in response["hits"]["hits"]:
                doc = hit["_source"]
                results.append({
                    "id": doc["id"],
                    "title": doc["title"],
                    "summary": doc.get("summary", ""),
                    "score": hit["_score"],
                    "metadata": {
                        "department": doc["department"],
                        "doc_type": doc["doc_type"],
                        "created_at": doc["created_at"],
                        "author": doc["author"]
                    }
                })
            
            return {
                "content": [{
                    "type": "text",
                    "text": f"找到 {response['hits']['total']['value']} 个结果:"
                }, {
                    "type": "text",
                    "text": self._format_search_results(results)
                }],
                "metadata": {
                    "total": response['hits']['total']['value'],
                    "page": page,
                    "page_size": limit
                }
            }
            
        except Exception as e:
            return {
                "content": [{
                    "type": "text",
                    "text": f"搜索失败: {str(e)}"
                }],
                "isError": True
            }
    
    async def handle_knowledge_upload(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """上传文档实现"""
        try:
            title = params["title"]
            content = params.get("content", "")
            metadata = params.get("metadata", {})
            
            # 检查权限
            user = metadata.get("author", "unknown")
            if not self.permission_mgr.check_permission(user, None, "write"):
                return self._error_response("权限不足")
            
            # 创建文档
            doc = Document(
                title=title,
                content=content,
                metadata=DocumentMetadata(
                    department=metadata.get("department", "general"),
                    security_level=metadata.get("security_level", "internal"),
                    tags=metadata.get("tags", []),
                    author=user
                ),
                file_hash=self._calculate_hash(content)
            )
            
            # 生成摘要
            doc.summary = await self._generate_summary(content)
            
            # 生成嵌入向量
            doc.embeddings = await self._generate_embeddings(content)
            
            # 存储文档
            self.documents[doc.id] = doc
            
            # 索引到搜索引擎
            await self._index_document(doc)
            
            # 存储到向量数据库
            await self._store_embeddings(doc)
            
            # 审计日志
            self._log_audit("upload", user, doc.id)
            
            return {
                "content": [{
                    "type": "text",
                    "text": f"文档上传成功！\nID: {doc.id}\n标题: {title}\n安全等级: {doc.metadata.security_level}"
                }],
                "suggestedToolCalls": [{
                    "toolName": "summarize_document",
                    "arguments": {"doc_id": doc.id}
                }]
            }
            
        except Exception as e:
            return self._error_response(f"上传失败: {str(e)}")
    
    async def handle_ask_question(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """文档问答实现"""
        try:
            question = params["question"]
            scope = params.get("scope", "all")
            include_sources = params.get("include_sources", True)
            
            # 向量搜索相关文档
            query_embedding = await self._generate_embeddings(question)
            similar_docs = await self._vector_search(query_embedding, top_k=5)
            
            if not similar_docs:
                return {
                    "content": [{
                        "type": "text",
                        "text": "没有找到相关文档来回答这个问题。"
                    }]
                }
            
            # 构建上下文
            context = "\n\n".join([
                f"文档: {doc.title}\n内容: {doc.content[:1000]}"
                for doc in similar_docs
            ])
            
            # 调用 LLM 生成答案
            answer = await self._call_llm_for_qa(question, context)
            
            # 构建响应
            response_content = [{
                "type": "text",
                "text": f"**问题**: {question}\n\n**答案**: {answer}"
            }]
            
            if include_sources:
                sources_text = "\n".join([
                    f"- {doc.title} (相关度: {score:.2f})"
                    for doc, score in similar_docs
                ])
                response_content.append({
                    "type": "text",
                    "text": f"\n**参考文档**:\n{sources_text}"
                })
            
            return {"content": response_content}
            
        except Exception as e:
            return self._error_response(f"问答失败: {str(e)}")
    
    # ========== 资源处理 ==========
    
    async def handle_resource_read(self, uri: str) -> Dict[str, Any]:
        """处理资源读取请求"""
        if uri.startswith("knowledge://recent/"):
            limit = int(uri.split("/")[-1]) if uri.split("/")[-1].isdigit() else 10
            recent_docs = sorted(
                self.documents.values(),
                key=lambda x: x.metadata.updated_at,
                reverse=True
            )[:limit]
            
            return {
                "contents": [{
                    "uri": uri,
                    "mimeType": "application/json",
                    "text": json.dumps([
                        {
                            "id": doc.id,
                            "title": doc.title,
                            "updated_at": doc.metadata.updated_at.isoformat(),
                            "author": doc.metadata.author
                        }
                        for doc in recent_docs
                    ], ensure_ascii=False, indent=2)
                }]
            }
        
        elif uri.startswith("knowledge://stats/overview"):
            stats = self._generate_statistics()
            return {
                "contents": [{
                    "uri": uri,
                    "mimeType": "application/json",
                    "text": json.dumps(stats, ensure_ascii=False, indent=2)
                }]
            }
        
        return {"error": f"未知资源: {uri}"}
    
    # ========== 辅助方法 ==========
    
    def _format_search_results(self, results: List[Dict]) -> str:
        """格式化搜索结果"""
        formatted = []
        for i, result in enumerate(results, 1):
            formatted.append(
                f"{i}. **{result['title']}**\n"
                f"   概要: {result['summary'][:100]}...\n"
                f"   部门: {result['metadata']['department']} | "
                f"类型: {result['metadata']['doc_type']} | "
                f"作者: {result['metadata']['author']}\n"
            )
        return "\n".join(formatted)
    
    def _generate_statistics(self) -> Dict:
        """生成统计信息"""
        total_docs = len(self.documents)
        departments = {}
        doc_types = {}
        
        for doc in self.documents.values():
            dept = doc.metadata.department
            doc_type = doc.metadata.doc_type
            
            departments[dept] = departments.get(dept, 0) + 1
            doc_types[doc_type] = doc_types.get(doc_type, 0) + 1
        
        return {
            "total_documents": total_docs,
            "by_department": departments,
            "by_type": doc_types,
            "last_updated": max(
                [doc.metadata.updated_at for doc in self.documents.values()],
                default=datetime.now()
            ).isoformat()
        }
    
    async def _generate_summary(self, content: str, length: str = "medium") -> str:
        """生成文档摘要（简化版）"""
        # 实际实现应该调用 LLM API
        sentences = content.split('.')
        if len(sentences) <= 3:
            return content
        
        if length == "short":
            return '.'.join(sentences[:2]) + '.'
        elif length == "detailed":
            return '.'.join(sentences[:10]) + '.'
        else:  # medium
            return '.'.join(sentences[:5]) + '.'
    
    async def _generate_embeddings(self, text: str) -> List[float]:
        """生成文本向量（简化版）"""
        # 实际实现应该调用嵌入模型 API
        import numpy as np
        # 使用简单的哈希作为模拟嵌入
        hash_val = int(hashlib.md5(text.encode()).hexdigest()[:8], 16)
        np.random.seed(hash_val)
        return np.random.randn(384).tolist()  # 384维向量
    
    async def _vector_search(self, query_embedding: List[float], top_k: int = 5):
        """向量搜索"""
        # 简化实现
        results = []
        for doc in self.documents.values():
            if doc.embeddings:
                # 计算余弦相似度
                similarity = self._cosine_similarity(query_embedding, doc.embeddings)
                if similarity > 0.7:  # 阈值
                    results.append((doc, similarity))
        
        # 按相似度排序
        results.sort(key=lambda x: x[1], reverse=True)
        return results[:top_k]
    
    def _cosine_similarity(self, a: List[float], b: List[float]) -> float:
        """计算余弦相似度"""
        import numpy as np
        a_np = np.array(a)
        b_np = np.array(b)
        return np.dot(a_np, b_np) / (np.linalg.norm(a_np) * np.linalg.norm(b_np))
    
    def _calculate_hash(self, content: str) -> str:
        """计算内容哈希值"""
        return hashlib.md5(content.encode()).hexdigest()
    
    def _log_audit(self, action: str, user: str, doc_id: str):
        """记录审计日志"""
        self.audit_log.append({
            "timestamp": datetime.now().isoformat(),
            "action": action,
            "user": user,
            "doc_id": doc_id,
            "ip": "127.0.0.1"  # 实际应该从请求获取
        })
    
    def _error_response(self, message: str) -> Dict[str, Any]:
        """错误响应"""
        return {
            "content": [{"type": "text", "text": f"错误: {message}"}],
            "isError": True
        }
    
    async def _call_llm_for_qa(self, question: str, context: str) -> str:
        """调用 LLM 进行问答（简化版）"""
        # 实际实现应该调用 LLM API
        prompt = f"""
        基于以下文档内容回答问题：
        
        文档内容：
        {context}
        
        问题：{question}
        
        答案：
        """
        # 这里应该调用实际的 LLM API
        return "这是基于文档内容生成的答案。"

# ========== MCP Server 主程序 ==========
async def main():
    server = KnowledgeBaseMCPServer()
    
    # 这里应该实现 MCP 协议的具体通信
    # 包括 initialize, tools/list, tools/call, resources/list, resources/read 等
    
    print("知识库 MCP Server 已启动")
    print("可用工具:")
    print("- knowledge_search: 搜索文档")
    print("- knowledge_upload: 上传文档")
    print("- get_document: 获取文档")
    print("- ask_question: 文档问答")
    print("- summarize_document: 文档摘要")
    print("- find_similar: 查找相似文档")

if __name__ == "__main__":
    asyncio.run(main())

五、配置文件示例

# config.yaml
server:
  name: "enterprise-knowledge-base"
  version: "1.0.0"
  host: "0.0.0.0"
  port: 8000
  auth_required: true

database:
  elasticsearch:
    hosts: ["localhost:9200"]
    index: "knowledge_docs"
  qdrant:
    host: "localhost"
    port: 6333
    collection: "doc_embeddings"
  redis:
    host: "localhost"
    port: 6379
    db: 0

embeddings:
  model: "text-embedding-ada-002"
  api_key: "${OPENAI_API_KEY}"
  dimensions: 1536

llm:
  model: "gpt-4-turbo"
  api_key: "${OPENAI_API_KEY}"
  temperature: 0.1

security:
  jwt_secret: "${JWT_SECRET}"
  token_expiry: 86400
  rate_limit:
    requests_per_minute: 60
  allowed_origins:
    - "https://claude.ai"
    - "http://localhost:*"

storage:
  document_store: "s3"
  s3:
    endpoint: "s3.amazonaws.com"
    bucket: "knowledge-docs"
  local_backup: "/var/backups/knowledge"

integrations:
  confluence:
    enabled: true
    base_url: "https://your-company.atlassian.net/wiki"
  github:
    enabled: false
  sharepoint:
    enabled: false

logging:
  level: "INFO"
  file: "/var/log/knowledge-mcp.log"
  audit_log: "/var/log/knowledge-audit.log"

六、部署与使用

1. 启动服务器

# 安装依赖
pip install mcp elasticsearch qdrant-client aiohttp pydantic

# 启动
python knowledge_mcp_server.py

2. Claude Desktop 配置

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "knowledge-base": {
      "command": "python",
      "args": [
        "/path/to/knowledge_mcp_server.py"
      ],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "ELASTICSEARCH_HOSTS": "localhost:9200"
      }
    }
  }
}

3. 使用示例

# 客户端调用示例
async with ClientSession(stdio_client(StdioServerParameters(
    command="python",
    args=["knowledge_mcp_server.py"]
))) as session:
    # 初始化
    await session.initialize()
    
    # 列出工具
    tools = await session.list_tools()
    print("可用工具:", [t.name for t in tools.tools])
    
    # 搜索文档
    response = await session.call_tool(
        "knowledge_search",
        arguments={
            "query": "Q4 销售报告",
            "filters": {
                "department": "sales",
                "security_level": "internal"
            }
        }
    )
    
    # 文档问答
    response = await session.call_tool(
        "ask_question",
        arguments={
            "question": "公司今年的销售目标是多少？",
            "scope": "sales"
        }
    )

七、扩展功能设计

1. 实时协作

文档协同编辑
评论和批注系统
变更历史追踪

2. 高级搜索

语义搜索增强
混合搜索（关键词+向量）
自然语言查询转换

3. 工作流集成

文档审批流程
自动化文档分类
过期文档清理

4. 分析与洞察

知识图谱构建
文档关联分析
知识缺口识别

5. 移动端支持

响应式设计
离线访问
移动端优化界面

八、安全与合规

数据加密
- 传输层：TLS 1.3
- 存储加密：AES-256
- 密钥管理：HSM/KMS
访问控制
- 基于角色的访问控制（RBAC）
- 属性基础的访问控制（ABAC）
- 多因素认证
合规性
- GDPR 数据主体权利
- SOX 文档保留策略
- HIPAA 医疗文档保护
- 审计日志保留 7 年
监控与告警
- 异常访问检测
- 数据泄露防护
- 实时告警系统

这个设计提供了一个完整的企业级知识库 MCP Server 解决方案，可以根据实际需求进行调整和扩展。

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