Muhammad Hamza Arshad Majeed

arXiv2026

An Empirical Study of Model Context Protocol Applications

The Model Context Protocol (MCP) standardizes how large language model applications communicate with external tools, but leaves the application side unspecified: unlike traditional dependencies resolved through package managers, developers integrating MCP servers face no conventions for configuration, communication, or human oversight. This ecosystem is also under-researched, with existing work focused on servers rather than the applications consuming them. We conduct a large-scale study of 1,723 MCPApps mined from GitHub. We first derive MCPAppTax from a representative sample, then use an LLM-assisted pipeline to apply it across the full dataset, characterizing server integration across configuration, SDK use, and human-in-the-loop mechanisms. Our results show that the ecosystem has converged on some practices but not others: most MCPApps configure servers using files (85.2%) and use an official SDK (81.1%) to communicate with servers, yet no naming convention has emerged for configuration files. Human oversight diverges most, logging (90.8%) and enable/disable controls (77.2%) are common, but only 37.2% gate tool execution behind a blocking approval step, leaving the LLM able to invoke any enabled tool unconditionally in most MCPApps.

cs.SE↗

arXiv2026

Interpretable Crisis Behavior Analysis Using Mobility and Social Media Data

Crises alter both how people move and how they communicate. During emergencies such as wildfires and pandemics, changes in mobility patterns and online emotional discourse evolve jointly, yet they are typically studied in isolation. This paper presents a unified and interpretable pipeline that integrates mobility and social media data to identify cross-domain behavioral patterns in crisis settings. The framework is evaluated through two case studies: a short-horizon analysis of the January 2025 Los Angeles wildfires (prototype case) and a longitudinal analysis of UAE COVID-19 behavior from March 2020 to December 2021 (primary case, 671 days). The pipeline aligns heterogeneous daily signals, transforms them into binary behavioral states, applies Formal Concept Analysis (FCA) to extract co-occurrence structure, mines association rules, and validates rule stability through chronological holdout testing. A structured policy-translation layer renders robust rules as operational briefs specifying triggers, lead times, and action playbooks. Results reveal clear cross-domain behavioral structure in both crises. In the wildfire case, traffic stress, fear/anger sentiment, and governance discourse are tightly coupled within a 33-day window, with key rules reaching 100\% confidence and lift scores up to 2.5. In the COVID case, repeated mobility adaptation and sentiment volatility yield 8 stable same-day rules (88\% holdout pass rate) and 40 clean predictive rules with 2--7 day lead horizons. The work demonstrates that interpretable multimodal fusion can produce both scientifically credible and policy-actionable crisis intelligence.

cs.CY↗