This tool automates the complex process of identifying technology convergence and disruption. It quantifies insights across 14 live data sources, including patent databases, academic research platforms, and job markets, using eight purpose-built statistical models. Designed as a Model Context Protocol (MCP) server, it allows AI agents like Claude Desktop or Cursor to directly call a single endpoint, eliminating the need for manual data triangulation, API management, and custom statistical implementations. This enables users to detect cross-domain patent convergence years in advance, trace knowledge cascades, and score industries against disruption frameworks with structured, actionable intelligence.
主要功能
01Christensen disruption scoring
02Branching process cascade model
03Log-logistic diffusion curve fitting
040 GitHub stars
05Bipartite patent convergence analysis
06Fiedler spectral skill clustering
