List the documentation sets in the index, with page counts and freshness.

Search the documentation. Returns ranked snippets, not full pages.

Use this first for any question about a documented tool. Follow up with read_doc on the paths it returns.

If a NOTE says one of your words missed, believe it. Matching is OR, so a distinctive word can be outvoted by the common ones next to it — the note names the pages that word really lives on. Read one before you conclude the feature does not exist.

Pass source whenever the question names one product. These doc sets cover the same ground in different words, so an unfiltered search spends slots on the wrong products: a question about Claude Code hooks will also return Cursor's and Codex's. Omit source only to compare products, or when you genuinely do not know which one holds the answer.

A search costs ~500 tokens. Budget for two. The first query is the one you can phrase; the second is the one the docs would. If the rows do not cohere around your question — they name adjacent features, or only things you already knew — do not answer from them. Guess what the docs call the thing and search again. limit which model an org member can select returns org roles and spend limits and warns about nothing; model access control — the docs' own name for it — returns the right page first. You can usually produce that name; the cost of trying is one more search.

Query in English. The indexed docs are English and matching is lexical, so a question in another language finds nothing — translate it to English keywords first ("훅 이벤트 목록" -> "hook events list").

Keyword-style queries work best and filler words are dropped. Symbols are fine here — AGENTS.md, PreToolUse, --flag-name, spec_version all match, because punctuation is treated as a word boundary rather than dropped. Do not reach for grep_docs just because the query contains one. There is no fuzzy matching, so a typo finds nothing.

Indexed sources: claude-code (Claude Code), codex (OpenAI Codex), cursor (Cursor), opencode (opencode), xai (xAI / Grok).

Read a documentation page, or one section of it.

path is what search_docs returns, e.g. "claude-code/en/hooks". Pass section (a heading or its anchor, at any depth) to read just that part — required for very large pages, which otherwise return an outline to choose from.

A section that is one huge table — en/settings § Available settings, en/env-vars § Variables — has no subheadings to outline, so it comes back in parts, each carrying the table's header row. The reply names the part count; pass part=2, part=3… for the rest, or use grep_docs to pull a single entry out of it.

The Related pages footer is the page's own outgoing cross-references — what its authors thought you should read next. Follow them when the question spans more than the one page you happened to land on.

Indexed sources: claude-code (Claude Code), codex (OpenAI Codex), cursor (Cursor), opencode (opencode), xai (xAI / Grok).

Regex search over the raw documentation markdown. Use search_docs first.

This is the last resort, not the first move. It returns raw matching lines, so it costs several times what a search costs and gives you no ranking — an unscoped grep for a common term burns ~1.5k tokens and still hits its cap.

Symbols are NOT a reason to come here: AGENTS.md, PreToolUse and --flag-name all match in search_docs. Come here only when search_docs missed, or when you need every occurrence of a literal — an env var, a config key, a flag — rather than the best passages about it.

pattern is a Python regex. Pass source unless you truly want all of them.

Indexed sources: claude-code (Claude Code), codex (OpenAI Codex), cursor (Cursor), opencode (opencode), xai (xAI / Grok).

List a source's pages — a cheap map of what exists.

Descriptions come with the listing while it is small. A large one is served as paths only, with the directory prefixes and their page counts, because the descriptions alone would cost more than fifteen searches. Pass prefix (one of the ones it names) to narrow it and get the descriptions back.

Indexed sources: claude-code (Claude Code), codex (OpenAI Codex), cursor (Cursor), opencode (opencode), xai (xAI / Grok).