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:

2. Install dependencies:

3. Set up environment variables:

4. Build the project:

Configuration

Create a .env file with your OpenAI API key:

Usage

As an MCP Server

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

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

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:

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

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