Keyword search over feedback text, ranked with BM25.

Args: query: free-text search query, e.g. "shift swap approval". source: optional exact filter: support_ticket, app_review, sales_call_note, or nps_comment. segment: optional exact filter: small_business, mid_market, or enterprise. limit: maximum number of results to return (default 20).

Returns feedback items ordered by relevance (highest BM25 score first), each as a dict with id, source, created_at, customer_segment, text, rating (if present), and score.

List feedback themes with at least min_items items, largest first.

Themes are computed deterministically from keyword and phrase overlap across feedback text (TF-IDF plus keyword-cluster grouping), with no LLM involved, so the same dataset always produces the same themes. Each theme's label is a trimmed first clause of its most representative real item (its "medoid"), not a synthetic phrase; representative_item_id names that item so you can look it up via feedback://item/{id}. Use get_theme(theme_id) for the full detail on one theme, including representative quotes.

Get one theme's member items, representative quotes, and source breakdown.

Args: theme_id: a theme id as returned by list_themes, e.g. "theme-01". Works for any theme the clustering produced, even one smaller than list_themes' default min_items.

Raises ValueError (surfaced to the client as a tool error) if the theme_id does not exist for the current dataset.

Tag one feedback item's severity (critical, high, medium, or low).

Args: feedback_id: an id from the dataset, e.g. "fb-0001".

Severity is rule-based, from keywords in the text, the item's rating if it has one, and its source. Returns the severity label, a numeric score, the keywords that matched, and a plain-language rationale. Raises ValueError if the id does not exist.

Summarize severity across the whole dataset.

Returns item counts by severity level, a breakdown by source and by customer segment, and the feedback ids of the ten highest- scoring critical/high items to look at first.

Draft a structured, PRD-style problem statement for one theme.

Args: theme_id: a theme id as returned by list_themes, e.g. "theme-01".

Returns who is affected (customer segments and counts), what the problem is (a generated one-line description), evidence (representative quotes with feedback ids), frequency (count, percent of dataset, source breakdown, date range, average rating), and a suggested success metric derived from the theme's own volume. Every field traces back to real items in the dataset; nothing is invented. Raises ValueError if the theme_id does not exist.