Why precision beats volume - the token rationale

Why precision beats volume - the token rationale

There's a concrete reason @-mention precision wins over pasting more code. An @-mention guarantees the file lands in context instead of hoping semantic search surfaces it - the model reads the exact file, finds the definitions and returns ground truth. @ isn't limited to files: reference a doc, or even a past chat (which summarizes it and reads the transcript, so you inherit the summary without dragging all prior context forward).

And there is a cost lever underneath the proximity rule from the rules section: rules and skills count toward tokens, but only when invoked. A skill costs nothing until the agent chooses it; an always-apply rule rides along on every submission. So glob-scope rules to the files they govern (e.g. *.tsx components) and they only load when a matching file is touched - keeping always-true rules out of unrelated context. Since Cursor 2.4, the harness's dynamic context discoveryThe agent pulling only the relevant parts of files, tools and MCP servers into context as needed, instead of loading everything up front. Press Enter for the full definition. does the same thing automatically: it pulls in only what it deems relevant rather than loading every file/tool/MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. output, which is part of how it cuts agent tokens by roughly 47% even on other providers' models.