Forensics Connection MCP Server

A Model Context Protocol (MCP) server that analyzes forensic evidence to identify people and their connections, then generates Python code for network visualization.

Features

• LLM-Powered Relationship Analysis: Uses OpenAI's GPT-4 to intelligently analyze relationships between people in evidence text
• Network Visualization: Generates comprehensive Python code using NetworkX and Matplotlib for interactive network graphs
• Evidence Parsing: Automatically extracts and categorizes different types of evidence (police reports, witness statements, communications, etc.)
• Connection Strength Scoring: Assigns relationship strength scores (1-10) based on evidence quality
• Cluster Analysis: Identifies groups and central figures in the network
• Flexible Filtering: Configurable minimum connection strength thresholds

Installation

1. Clone the repository:

git clone <repository-url>
cd mcp-forensics

2. Install dependencies:

npm install

3. Set up environment variables:

cp .env.example .env
# Edit .env and add your OpenAI API key

4. Build the project:

npm run build

Configuration

Create a .env file with your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here

Usage

As an MCP Server

The server can be used with any MCP-compatible client:

npm start

Available Tools

analyze_connections

Analyzes evidence text to identify people and their connections.

Parameters:

• evidence (required): The evidence text to analyze
• options (optional):
  • includeVisualization (boolean, default: true): Whether to include Python visualization code
  • minimumConnectionStrength (number, default: 3): Minimum connection strength to include (1-10)
  • groupByOrganization (boolean, default: true): Whether to group people by organization

Returns:

• List of people identified with their organizations and roles
• Connections between people with strength scores and evidence
• Central figures in the network
• Clusters/groups of related people
• Python code for network visualization

Example Usage

// Example evidence input
const evidence = `
Police Report #001: Incident occurred at 123 Main St on January 15, 2024 at 2:30 PM. 
Witness John Smith reported suspicious activity. Vehicle license plate ABC123 was seen leaving the scene.

Email Chain - Subject: Concerns about OpenAI: 
From: elon.musk@x.com To: satya.nadella@microsoft.com, mark.zuckerberg@meta.com 
"I'm increasingly concerned about Sam's strategic direction..."

Meeting Notes - Private Tech Leaders Dinner: 
Attendees: Musk, Nadella, Zuckerberg. Topic: OpenAI concerns.
`;

// The server will return detailed analysis and Python visualization code

LLM-Enhanced Relationship Analysis

The system uses OpenAI's GPT-4 to analyze relationships with sophisticated context understanding:

• Direct Communications: Emails, messages, calls (strength: 9-10)
• Face-to-Face Meetings: Shared events, professional collaboration (strength: 7-8)
• Group Communications: Shared concerns, indirect interactions (strength: 5-6)
• Professional Associations: Mentioned together, weak connections (strength: 3-4)
• Coincidental Mentions: Very weak connections (strength: 1-2)

Python Visualization Output

The generated Python code includes:

• Network Graph: Interactive visualization with NetworkX and Matplotlib
• Organization Grouping: Color-coded nodes by organization
• Connection Types: Different edge styles for different relationship types
• Centrality Analysis: Node sizes based on degree centrality
• Statistical Report: Network statistics and key findings
• Customizable Layout: Spring layout for optimal node positioning

Required Python Dependencies

The generated code requires:

pip install networkx matplotlib numpy

Development

Scripts

• npm run build: Compile TypeScript to JavaScript
• npm run dev: Build with watch mode
• npm start: Run the compiled server
• npm run inspector: Run with MCP inspector for debugging

Project Structure

src/
├── index.ts              # Main server entry point
├── types.ts              # TypeScript type definitions
├── evidenceParser.ts     # Evidence parsing and LLM analysis
├── pythonGenerator.ts    # Python code generation
└── ...

Connection Types

The system identifies several types of connections:

• communication: Direct communications (emails, messages)
• meeting: In-person meetings and events
• witness: Witness/observer relationships
• location: Geographic/location-based connections
• organization: Same organization affiliations
• other: General associations and mentions

Error Handling

• Fallback to pattern-based analysis if LLM calls fail
• Rate limiting protection for OpenAI API calls
• Comprehensive error reporting and logging
• Graceful degradation for missing data

License

MIT License