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LSPD Interrogation MCP Server: Real-Time Data & Streamlined Workflows - MCP Implementation

LSPD Interrogation MCP Server: Real-Time Data & Streamlined Workflows

🚨 LSPD Interrogation MCP Server: Streamline officer agent collaborations with centralized real-time data, interrogation workflows, and PoC-driven solutions for mission-critical operations.

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About LSPD Interrogation MCP Server

What is LSPD Interrogation MCP Server: Real-Time Data & Streamlined Workflows?

This server is an advanced simulation tool for police interrogation training, powered by the Model Context Protocol (MCP) and OpenAI's GPT-3.5-turbo. It enables real-time data processing and optimized workflows to simulate high-stakes interrogations. By integrating dynamic resource management and AI-driven dialogue generation, it provides realistic scenarios for testing strategies, evaluating suspect responses, and refining investigative techniques.

How to use LSPD Interrogation MCP Server: Real-Time Data & Streamlined Workflows?

Start by installing dependencies with pnpm install, then configure your OpenAI API key in the .env file. Launch the server and interact via RESTful endpoints. For example, retrieve an officer profile with GET /profile/1234, initiate an interrogation by POSTing suspect details with pressure levels and evidence, and dynamically adjust responses using the /respond endpoint. All inputs undergo strict validation to ensure integrity and security.

LSPD Interrogation MCP Server Features

Key Features of LSPD Interrogation MCP Server: Real-Time Data & Streamlined Workflows?

  • MCP-Powered Architecture: Leverages SDK for seamless HTTP communication and adaptive resource allocation between officer profiles and interrogation simulations
  • AI-Driven Realism: Generates context-aware interrogation strategies and personality-specific suspect reactions (e.g., a "cowardly" suspect might panic under high pressure levels)
  • Scenario Customization: Supports integration of crime types (armed robbery, drug trafficking), evidence lists, and adjustable guilt thresholds to tailor training complexity
  • Operational Efficiency: Streamlines workflows through type-safe API endpoints and configurable parameters like temperature (creativity) and token limits

Use cases of LSPD Interrogation MCP Server: Real-Time Data & Streamlined Workflows?

Trainers can simulate high-pressure scenarios where officers practice balancing ethical boundaries with coercive tactics. For instance:

  • Test how a suspect with "defensive" personality traits reacts to presented evidence like fingerprints
  • Practice adapting questioning styles based on dynamically generated suspect responses
  • Analyze how varying pressure levels (0-100 scale) affect confession likelihood metrics
  • Validate evidence presentation strategies using structured crime type parameters

LSPD Interrogation MCP Server FAQ

FAQ from LSPD Interrogation MCP Server: Real-Time Data & Streamlined Workflows?

  • Q: How is sensitive data protected? A: OpenAI keys are stored via environment variables, HTTPS is enforced in production, and all inputs are validated using Zod schemas
  • Q: Can I customize AI models? A: Yes, configuration options allow switching models and adjusting parameters like token limits (configurable in config.ts)
  • Q: What happens if invalid data is submitted? A: The server returns clear validation errors for issues like out-of-range pressure values or malformed evidence arrays
  • Q: How do I contribute improvements? A: Fork the repo, create a feature branch, and submit pull requests following our contribution guidelines

Content

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.

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