Revised version

This report analyzes the NOAA Storm Database to identify the weather events that have caused the greatest impacts on public health and the economy in the United States. The analysis is fully reproducible in R and is based on the original storm data file.

The Model Context Protocol (MCP) is rapidly becoming an interoperability layer through which AI agents discover and invoke external data, tools, and workflows. Digital-library research, however, requires more than protocol conformance: executable services must also be semantically interpretable, procedurally reusable, and institutionally accountable. This study analyzes 16,800 latest-version records from the Official MCP Registry and a deterministic sample of 250 linked GitHub repositories, of which 212 were successfully retrieved. It combines registry analysis, static capability extraction, computational text analysis, network measures, and qualitative validation to examine capability metadata, agent skills, institutional signals, and provenance. The findings show an ecosystem organized more successfully for discovering callable services than for interpreting and governing knowledge work. Tools dominate detected capabilities; retrieval dominates classifiable functions; explicit library, archive, and museum descriptions remain marginal; skill documents are procedurally rich but highly concentrated; and 96.7% of Registry records contain none of six measured provenance cues. The paper develops an executable knowledge ecosystem framework and argues that MCP capability descriptions constitute metadata with operational consequences. It concludes with an institutional roadmap for curating services, governing reusable skills, implementing cross-layer provenance, and preserving meaningful human oversight.