AI 客服商城 MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@AI 客服商城 MCP Server帮我搜索一下店里有没有无线鼠标,推荐一款"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI 客服商城 MCP Server
一款将 大模型客服 + 商品数据 + 安全审计 打包成 MCP 服务的综合练习项目。 面向 AI Agent 开发,覆盖 LLM API、Function Calling、RAG、MCP 协议、Web 安全审计、人工审核闭环。
✨ 功能总览
1. 智能客服(LLM + RAG + Function Calling)
DeepSeek 大模型驱动,
system prompt角色+规则+知识注入RAG 检索增强:FAQ 知识库(退货/保修/发票/物流等 13 条)按需检索,注入上下文
函数调用:模型自主决定调用
search_products查商品(名称/价格/库存/描述)自动转人工:AI 回答不了时自动建议转人工 + 按钮高亮
人工客服后台:排队队列 / 接单 / 回复,状态机
ai → queue → human
2. 商品网页
商品卡片网格 + 实时搜索(模糊 2 字滑窗匹配)
用户登录(演示靶场版)+ 可拖动客服聊天窗口
3. MCP Server(4 个工具)
工具 | 说明 |
| 按关键词搜索商品 |
| 按 id 查商品详情 |
| 调用完整 AI 客服 |
| 8 项安全审计 + 证据定位 |
4. 安全审计闭环(特色亮点)
AI 审计(8 项检测,含静态+动态)
→ 证据定位(文件/行号/代码上下文字符串)
→ 人工审核台(/review): 确认 / 误报 / 修复
→ 修复方案(修复前后代码对照)
→ 状态追踪(new → confirmed → fixed) + 修复率看板审计检测项:
✅ 明文密码存储(文本比对)
✅ 用户枚举(错误消息差异)
✅ 登录限流缺失
✅ Token 无过期
✅ IDOR 越权(跨用户订单)
✅ 敏感信息泄露(无鉴权返回密码)
✅ 任意改密码(无旧密码校验)
✅ 硬编码密钥扫描
⚠️ 安全说明:项目内登录/订单接口故意保留典型漏洞,作为安全审计练习靶场。仅供本地学习演示,切勿部署公网或用于真实系统。
Related MCP server: CommerceHub MCP
🚀 快速开始
1. 环境准备
# Python 3.10+,创建虚拟环境
cd ai-customer-service-mcp
python -m venv .venv
.venv\Scripts\activate # Windows
pip install -r requirements.txt # flask requests python-dotenv mcp beautifulsoup4 lxml2. 配置 LLM(可选,未配置走规则版)
# 复制 .env.example 为 .env,填入 DeepSeek API Key
# deepseek 平台: https://platform.deepseek.com/3. 启动网页服务器
python server/app.py
# 打开 http://127.0.0.1:5000(商城+客服窗)
# 客服后台: http://127.0.0.1:5000/agent
# 审核台: http://127.0.0.1:5000/review4. 测试账号
admin / admin123 (管理员)
test / 123456 (普通用户)5. 启动 MCP Server(stdio,供 MCP 客户端调用)
python server/mcp_server.pyopencode 配置示例(opencode.json):
{
"mcp": {
"shop": {
"type": "local",
"command": ["python", "server/mcp_server.py"],
"cwd": "D:/lesson/ai-customer-service-mcp",
"enabled": true
}
}
}🧭 项目结构
ai-customer-service-mcp/
├── server/
│ ├── app.py # Flask 路由(网页/API/审核/转人工)
│ ├── mcp_server.py # MCP Server(@mcp.tool() 4 工具)
│ ├── data_service.py # 商品数据服务
│ ├── auth_service.py # 登录认证(靶场版)
│ ├── order_service.py # 订单服务(IDOR 靶点)
│ ├── chat_service.py # 客服(LLM+RAG+Function Calling)
│ ├── llm_client.py # LLM API 客户端(OpenAI 兼容)
│ ├── rag_service.py # 知识库检索(Cosine 相似度)
│ ├── audit_service.py # 安全审计(8 项+证据定位)
│ ├── review_service.py # 审核闭环(状态/修复方案/统计)
│ ├── human_service.py # 转人工会话状态机
│ └── templates/ # index.html / agent.html / review.html
├── data/
│ ├── products.json # 商品
│ ├── users.json # 用户(明文靶点)
│ ├── orders.json # 订单(IDOR 靶点)
│ └── faq_knowledge.json # 客服 FAQ 知识库
├── crawler/ # 商品爬虫(规划)
├── web/ # 前端资源(规划)
├── .env.example # LLM 配置模板
└── .gitignore # 保护 .env 不提交🎯 设计要点(面试可讲)
RAG:chunk → 向量化(词频/cosine)→ Top-K → 注入 system prompt;生产可换 embedding + FAISS
Function Calling 协议:assistant
tool_calls与role=tool消息配对(亲身踩坑:缺 assistant 记录会 400)MCP 2.x:
MCPServer+@tool()装饰器,stdio 通信,被 opencode 实测调用成功安全闭环:检测(静态+动态黑盒)→ 证据(文件/行号/上下文)→ 人工审核(确认/误报)→ 修复方案 → 状态追踪
兜底设计:AI 答不上 → 自动建议转人工;用户排队 → 接单 → 回复 → 轮询展示
📜 License
MIT License
本项目所有数据均为本地模拟,演示账号与漏洞为教学靶场设计。仅供学习交流。
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