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.

Math problems of the day for 7/16/26.

Storm data analysis and identification of top event types for Population Health and Economic damages

This report is an early checkpoint on the way to building a text prediction app (the kind that suggests the next word as you type, similar to the predictive keyboard on a smartphone). The goal here is not the finished app — it’s to show three things: I can download, load, and work with the raw text data. I understand the basic shape and size of the data (how much there is, how it’s structured). I have a clear, practical plan for turning this data into a working word-prediction model and a simple app. Everything below uses three text files — collected from blogs, news articles, and Twitter posts — all in US English, provided as the source data for this project.