You ask your coding agent to add authentication with a popular library. It writes fifty lines of confident, clean code. None of it compiles, because the API it used was deprecated two major versions ago.
This is the stale-docs loop, and if you use coding agents daily you have lived it: the agent writes outdated code, you paste the error, it apologizes and writes slightly different outdated code, and you burn twenty minutes teaching a model about a breaking change it has never seen.
The fix is not a smarter model. It is giving the agent a way to pull current, versioned documentation at the moment it needs it.
Why Coding Agents Write Against Old APIs
Three things stack up against you.
First, training cutoffs. Every model froze its knowledge months before you started typing. If your library shipped a major version since then, the model literally cannot know about it.
Second, popularity bias. Models have seen far more code written against old versions than new ones, because old versions have been around longer. Ask about React Router or Pydantic and the model gravitates toward the API that dominates its training data, not the one in your package.json.
Third, confident interpolation. When a model half-remembers an API, it does not say "I'm not sure." It fills the gaps with plausible-looking method names. client.completions.create becomes client.complete, an option gets renamed, a required parameter goes missing. The code looks right, which is worse than looking wrong.
In my experience the third one causes the most wasted time, because the errors are subtle enough that you debug your own code before suspecting the agent's.
The Fixes That Do Not Actually Fix It
Before getting to what works, it is worth saying why the common workarounds fall short.
Pasting docs into the prompt. Works once. Then the conversation ends, the context is gone, and tomorrow you paste again. It also eats a huge slice of your context window whether or not the agent needs those specific pages.
Waiting for the next model release. The cutoff moves forward, but so do the libraries. You are always chasing.
Building a documentation knowledge base. Scraping a docs site into a vector store or a project knowledge base is genuinely useful for stable internal docs. But for fast-moving open source libraries it recreates the original problem one layer up: your snapshot goes stale, and now you own a re-indexing pipeline. Versioning makes it worse. If your KB has docs for v3 and your project uses v4, retrieval will happily hand the agent the wrong version with full confidence.
The pattern in all three: they treat documentation as something you load in advance. Current docs are a moving target, so anything loaded in advance decays.
What Works: Fetching Docs On Demand
The workflow that actually breaks the loop, popularized by tools like Context7, flips the direction. Instead of pre-loading documentation, the agent fetches it at the moment of need:
- The agent hits a library it needs to use.
- It resolves which library and, critically, which version your project depends on.
- It fetches the current docs for that exact version, live, from the source.
- It writes code with those pages sitting in context.
The docs are always current because they are retrieved seconds before use. There is no index to refresh, no snapshot to trust, and the version question is answered by your lockfile instead of by whatever the retriever found.
This is the same shift search made for LLMs generally: from "hope it memorized the answer" to "let it look the answer up." For coding agents, documentation is the highest-value thing to look up.
Wiring It Up
You need two pieces: a tool the agent can call to fetch web content, and instructions telling it when to use that tool.
For the first piece, the cleanest route is an MCP server that exposes fetch and search tools. The link.sc MCP server gives Claude Code, Cursor, and other MCP clients a fetch tool that returns any docs page as clean Markdown, which matters because raw HTML from a modern docs site is mostly navigation and script tags. I covered the client setup in MCP web tools for Claude and Cursor, and the Claude-specific walkthrough lives in Connect Claude to the web.
For the second piece, add a standing rule to your agent's project instructions (CLAUDE.md, .cursorrules, or equivalent):
## Library documentation
Before writing code against any external library:
1. Check the installed version in package.json / lockfile.
2. Fetch the docs for that version before writing code.
Prefer versioned URLs, e.g. /docs/v4/... over /docs/latest/...
3. If an API call fails, fetch that library's changelog or
migration guide before retrying.
Step 3 is the one most people skip and the one with the best payoff. A migration guide is the densest possible description of exactly what broke between the version the model remembers and the version you run. One fetched page usually ends the apology loop instantly.
If you would rather script it than configure an editor, the fetch call itself is one request:
curl "https://api.link.sc/fetch?url=https://docs.pydantic.dev/2.11/migration/&format=markdown" \
-H "Authorization: Bearer $LINKSC_API_KEY"
Markdown output keeps the token cost sane. A docs page that weighs 400KB as HTML often lands under 8KB as Markdown, and if you are paying per token that difference compounds fast. More on that trade-off in token optimization for feeding web data to LLMs.
How the Approaches Compare
| Approach | Freshness | Version-aware | Ongoing maintenance | Token cost per task |
|---|---|---|---|---|
| Model's training data | Months stale | No | None | Zero, but wrong |
| Paste docs into prompt | Fresh once | Manual | You, every session | High |
| Prebuilt docs KB / RAG | Stale between re-indexes | Rarely | Re-indexing pipeline | Medium |
| On-demand fetching | Live | Yes, via versioned URLs | None | Low, pages fetched as needed |
The honest caveat: on-demand fetching adds a few seconds of latency per lookup, and it depends on the docs site being reachable. For a private internal wiki behind a VPN, a prebuilt knowledge base is still the right call. For public, versioned, fast-moving library docs, which is where coding agents spend most of their time, fetching wins.
Three Habits That Make It Stick
Pin the version in the conversation. Start tasks with "we are on Next.js 15.3" rather than letting the agent assume. Cheap insurance even with fetching enabled.
Point at llms.txt when it exists. Many docs sites now publish an llms.txt index of their most useful pages. Fetching that first gives the agent a map instead of making it guess URLs.
Fetch changelogs on any deprecation warning. Do not wait for a hard failure. A deprecation warning means the docs the model remembers are already wrong; refresh before the next edit.
The stale-docs loop is not a model quality problem. It is a plumbing problem, and one tool call fixes it.
Give your coding agent live access to current docs: get a free link.sc API key and connect the MCP server in under five minutes.