LSPD Interrogation MCP Server

A Model Context Protocol (MCP) based police interrogation simulation server powered by OpenAI.

📌 Key Features

• MCP Integration:

  • Built using Model Context Protocol SDK
  • HTTP transport support
  • Dynamic resource management (officer-profile, conduct-interrogation)

• OpenAI Integration:

  • Uses GPT-3.5-turbo model
  • Generates dynamic interrogation strategies
  • Simulates suspect responses
  • Creates realistic dialogue flows

• Core Components:

  • Police officer profile management
  • Smart interrogation mechanics
  • Suspect behavior simulation
  • Crime type and evidence integration

🚀 Installation

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

⚙️ Configuration

.env file:

OPENAI_API_KEY=your_api_key_here

Configurable parameters in config.ts:

• AI model selection
• Maximum token count
• Temperature parameter (creativity level)

🌐 API Endpoints

Officer Profile

GET /profile/:badgeNumber

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

Start Interrogation

POST /interrogations/{suspectId}

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

Suspect Response

POST /interrogations/{suspectId}/respond

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

🔍 Example Usage

# 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"]
  }'

✅ Data Validation

All endpoints include strong type checking and validation using Zod library:

• Pressure Level: 0-100 (required)
• Suspect Name: string format
• Evidence: string array (optional)

🔒 Security

• Sensitive data (OpenAI API key) managed through environment variables
• HTTPS enforcement in production
• Secure input handling with request validation

🤝 Contribution

1. Fork the repository
2. Create new branch (feat/my-feature or fix/issue-number)
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

📜 License

Distributed under the MIT License.