The author reflects on a series of posts about "Context over MCP," a system designed to provide AI agents with project context even when they lack a traditional working directory. Key lessons learned include the need to serve context via discoverable tools and resources rather than relying on filesystem conventions like AGENTS.md, the importance of automating publishing processes, and the distinction between prose for instructions and structured data for facts. The series also highlights that browser-based agents face similar context limitations and that HTML forms can be leveraged as tools. Finally, the author emphasizes making agent context visible through "context cards" and integrating it with "Skills over MCP" to provide agents with both capabilities and project-specific information. AI
IMPACT Provides insights into delivering context to AI agents, potentially improving their functionality in environments without traditional file systems.
RANK_REASON The item is a wrap-up post reflecting on lessons learned from a series about a technical concept, rather than a new release or significant industry event.
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