get_answer

Collapses search → read → reason into one round-trip. The tool retrieves the most relevant wiki pages, enriches the top hits with symbol docstrings and source excerpts, hands them to the LLM, and returns a synthesized answer with citations and a confidence label. If confidence is low, it returns ranked excerpts and fallback file paths instead — letting the agent decide whether to dig deeper.

When to call

• Always first on any code question — before search_codebase or manual file exploration.
• If confidence is "medium" or "low", follow up with search_codebase and get_context on the fallback_targets.
• Works best when the question names explicit identifiers (a class, function, or module name).

Parameters

Returns

Example

get_answer("how does the authentication flow work?")
get_answer(
    "how are database migrations handled?",
    scope="backend/",
)

Things worth knowing

• Three confidence gates keep the tool honest:
  1. Dominance ratio — if the top retrieval score isn't 1.2× the second, synthesis is skipped and excerpts returned instead.
  2. Hedge-phrase detection — if the LLM admits insufficiency in its own answer, confidence is downgraded high → low.
  3. Identifier-citation gate — if the question names symbols but none appear in the top retrieval hits, confidence is downgraded high → medium.
• Question-aware symbol promotion — symbols matching identifiers extracted from the question get longer docstrings (400 chars) and a 40-line source body excerpt, so "how does X work?" questions can be answered without hedging.
• Intersection retrieval — relational questions ("how does X talk to Y?") are split into two queries; hits appearing in both get a 2× boost.
• Caching — questions are normalized and hashed; repeat questions hit the answer cache instantly. Hedged cached answers are bypassed for re-synthesis when the symbol pipeline is updated.
• No LLM provider configured? Falls back to retrieval-only — ranked hits + snippets at confidence="low".