Find issues semantically similar to a free-text query.

Ranked by exact cosine similarity over sentence-embeddings of title + body. Use this to answer "has anything like this been reported?" -- then cite the returned issue numbers.

Retrieve likely-duplicate candidates for a new issue's text.

Returns the k nearest issues, each flagged for whether it cleared the threshold. The threshold is an uncalibrated heuristic -- see the caveat field -- so treat flagged items as candidates to read, not as confirmed duplicates, and cite issue numbers when reporting them.

Predict which component/area an issue belongs to, by two methods.

Reports the logistic-regression prediction and the embedding-kNN vote separately, along with the neighbour issues behind the vote, so you can see whether the methods agree and what evidence supports them. measured_quality carries the scores from the evaluation run that produced this classifier -- quote those rather than assuming the prediction is reliable.

Look up one issue by number and return its stored metadata.

Full triage pass over new issue text: duplicates plus component routing.

One call combining find_duplicates and classify_component, with every supporting issue number collected into cited_issue_numbers. Narrate this for the user -- including the caveats -- rather than presenting it as a decision already made.

Describe what this server is serving: corpus coverage, model, and measured quality.

Call this before relying on the other tools -- it says what date range the corpus covers, so you can tell whether a "no duplicates found" answer means "none exist" or "none within the ingested window".