LSPD 询问 MCP 服务器

由 OpenAI 提供支持的基于模型上下文协议 (MCP) 的警察审讯模拟服务器。

📌 主要特点

MCP集成：
- 使用模型上下文协议 SDK 构建
- HTTP 传输支持
- 动态资源管理（警官档案、行为审讯）
OpenAI集成：
- 使用 GPT-3.5-turbo 模型
- 生成动态审讯策略
- 模拟嫌疑人的反应
- 创造真实的对话流程
核心组件：
- 警员档案管理
- 智能审讯机制
- 嫌疑人行为模拟
- 犯罪类型与证据整合

🚀 安装

pnpm install # Required environment variables cp .env.example .env # Start server pnpm start

⚙️ 配置

.env文件：

OPENAI_API_KEY=your_api_key_here

config.ts中可配置的参数：

AI模型选择
最大令牌数
温度参数（创意水平）

🌐 API 端点

官员简介

GET /profile/:badgeNumber

curl http://localhost:3000/profile/1234

开始审讯

POST /interrogations/{suspectId}

{ "suspectName": "John Doe", "pressureLevel": 75, "crime": "Armed robbery", "evidence": ["Fingerprint", "Security camera footage"] }

嫌疑人回应

POST /interrogations/{suspectId}/respond

{ "suspectName": "John Doe", "officerStatement": "Your fingerprints were found at the crime scene!", "guilt": 85, "personality": "cowardly", "previousResponses": ["I'm innocent!"] }

🔍 使用示例

# Get officer profile curl http://localhost:3000/profile/1234 # Start interrogation curl -X POST http://localhost:3000/interrogations/suspect_01 \ -H "Content-Type: application/json" \ -d '{ "suspectName": "John Doe", "pressureLevel": 80, "crime": "Drug trafficking", "evidence": ["Search records", "Confidential witness statement"] }'

✅ 数据验证

所有端点都包括使用 Zod 库的强类型检查和验证：

压力等级：0-100（必填）
嫌疑人姓名：字符串格式
证据：字符串数组（可选）

🔒 安全

通过环境变量管理的敏感数据（OpenAI API 密钥）
生产环境中的 HTTPS 强制执行
通过请求验证来确保输入处理的安全

🤝 贡献

分叉存储库
创建新分支（ feat/my-feature或fix/issue-number ）
提交你的更改
推送到分支
打开拉取请求

📜 许可证

根据 MIT 许可证分发。

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

模拟警察审讯的模型上下文协议服务器，使用户能够创建警官档案并根据压力水平、证据和犯罪类型等可配置参数对模拟嫌疑人反应进行动态审讯。

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