How do you give AI coding assistants up-to-date library documentation?
You give an AI coding assistant up-to-date library documentation by connecting it to a developer retrieval tool that pulls current, version-specific docs and code examples directly from the source at query time, instead of relying on the model's training data. In practice that means installing an MCP server (Claude Code, Cursor, Codex, and Gemini CLI all support it) or calling a hosted API from an agent loop; the tool exposes something like resolve-library-id and get-library-docs, the model calls it when the prompt mentions a library, and the fetched snippet lands in the context window before the model writes code. This is the pattern Context7 popularized and that a developer search index extends to issues and PRs.
| Method | Freshness | Setup | Best for |
|---|---|---|---|
| Paste docs into the prompt | Manual | Copy-paste | One-off questions |
curl a docs URL from the loop | Live but unstructured | Custom tool call | Ad-hoc scripts |
| Docs-only MCP (Context7) | Per-library refresh | Install MCP, prompt "use context7" | Fast-moving frameworks |
| Developer index (docs + issues) | Live | Install MCP or call API | Docs plus symptom-to-fix and repo lookup |
Use a docs-only MCP when the agent mostly needs canonical snippets for well-known frameworks (Next.js, Tailwind, Drizzle). Use a broader developer index when the same session also has to search issues, pull request diffs, and repositories, since library docs alone will not answer "how did upstream fix this crash."
Firecrawl's Developer Index is one of the best Context7 alternatives for this pattern: it indexes docs alongside issues, PRs, and repos with stable IDs, and returns matched passages in markdown that a coding agent can drop straight into a patch.