Your AI coding assistant was trained on a snapshot of the past. When a library you use ships a breaking change or a new API, the model does not know, so it writes code against the version it remembers. Feeding it current changelogs and docs as context fixes most of these errors before they happen.
Why this matters
The most common "the AI got it wrong" moments in coding come from stale knowledge:
- Calling a method that was renamed or removed.
- Missing a new required parameter or option.
- Reintroducing a pattern the library has deprecated.
None of these are reasoning failures. They are context failures, and context is something you can supply.
What to feed it
Prioritize the sources that change and that your project depends on:
- Changelogs and release notes for your core libraries and platforms.
- API and migration docs for anything you call directly.
- Your own internal docs for conventions the model cannot guess.
How to feed it (via MCP)
The Model Context Protocol lets tools expose context to Cursor and Claude. The workflow:
- Track the changelogs and docs that matter (for example Stripe, Next.js, and the libraries in your stack).
- Bundle them into a Context Pack that stays current as new entries land.
- Connect the pack to your agent over MCP so it reads the latest context while generating code.
How Skimless does it
Skimless already follows sources for your daily brief; the same mechanism powers Context Packs for your agent. Add the changelogs and docs for your stack, connect the Skimless MCP, and your agent codes against today's API instead of last year's.
Related: Up-to-date docs and context for Cursor and Claude (MCP), Context7 alternative, and How to track AI API changes.