Three modes, depending on which of area_id/dataset are given -- consolidates what were three separate tools (list_datasets, list_area_datasets, get_dataset_for_area) behind one, since they are all really "how do I get data" at different levels of specificity:

1. Neither area_id nor dataset: lists every dataset (stat) StatsMapped tracks for one country ('ireland' or 'united-kingdom'), with its key, human label, and which geography levels it can be shown at. Ireland and the UK track genuinely different datasets -- call this first for the right country before assuming a stat_key exists there, to find the right stat_key for compare's ranking mode.

2. area_id given, dataset omitted: lists every dataset available for that one area (e.g. "county:kerry" for Ireland, "uk:lad:e09000033" for the UK), with its latest figure, year-on-year change, and caveat labels only (not full caveat text -- use mode 3 for the full detail on any one dataset that matters). area_id comes from list_areas; country must match whichever country that call used, or this simply 404s ("unknown geography").

3. Both area_id and dataset given: full detail for one dataset in one area -- the latest figure, a written summary, full caveat text, and (if history_months is set) recent history. dataset is a series_key from mode 2's own response. history_months means actual months of history (0 = everything) -- e.g. 24 returns 2 years of an annual series, not 24 years. country must match area_id's own country.

   dataset and history_months are only meaningful together with area_id (and, for history_months, dataset too, since it only applies to mode 3); giving either without its real precondition raises rather than silently dropping the argument and dispatching to the wrong mode.

List every geography at one boundary level, for one country ('ireland' or 'united-kingdom'). level defaults to "county" (Ireland's 26 counties); the UK's own primary level is "lad" (local authority districts), not "county". Other levels exist per country (e.g. Ireland's "local_authority", "garda_division") -- see a dataset's own compatible_levels from query_data for which levels a given stat is actually published at. Returns each area's id (used by query_data's area-scoped modes, always paired with the SAME country) and name.

Four modes, depending on which arguments are given -- consolidates what were four separate tools (rank_areas, list_comparisons, get_comparison, check_comparability) behind one, since they are all really "how does this stat compare" at different scopes. Exactly one mode's arguments should be given; mixing arguments from different modes (e.g. both stat_key and pair_key, or only one of stat_key_a/stat_key_b) raises an error rather than silently guessing which mode was meant.

1. stat_key alone (no pair_key, no stat_key_a/stat_key_b): ranks every area at one geography level by its latest figure for that stat, for one country -- e.g. "which counties have the highest median sale price" (country="ireland"). stat_key comes from query_data's dataset-listing mode, for the SAME country. level omitted uses this ranking's own default level; pass one of that dataset's own compatible_levels for a different one -- a level this ranking doesn't have registered returns an empty list rather than an error. Where the underlying stat has no honest per-area denominator (crime, homelessness, live_register and similar -- StatsMapped's own RANKING_NO_DENOMINATOR_STATS), each row's rate_per_1000 is the real figure to rank/compare by, not latest_value, which is a raw count dominated by area population size. Always carry forward every entry in caveats when using a row in an answer.

2. pair_key alone: full detail for one registered comparison pair -- each axis's label, unit and publisher, the correlation stats (r, rho, and a leave-one-out sensitivity range naming the single most influential area), and caveats. pair_key comes from mode 4's own response, for the SAME country.

3. Both stat_key_a and stat_key_b given: does StatsMapped have a registered, hand-vetted comparison between these two stats? Registry-backed only -- never computes a fresh correlation for an arbitrary pair. Both stat_keys come from query_data's dataset- listing mode, for the SAME country. comparable is one of "yes" (a real, hand-vetted registered pair -- only this case may be treated as a confirmed relationship), "no" (a real structural impossibility, the two stats share no geography level at all), or "unknown" (not registered, not ruled out either -- StatsMapped genuinely hasn't vetted this pair; never treat this as "probably comparable"). Read reasons before deciding how to present any answer other than "yes".

4. None of the above given: lists every registered cross-dataset comparison pair for one country -- e.g. "median sale price vs new dwelling completions per 1,000 residents". A small, hand-curated set, not an arbitrary-pair engine: pass one of the returned pair_key values to mode 2 for the real correlation and axis detail.

   level is only meaningful together with stat_key (mode 1); giving it without stat_key raises rather than silently dropping it and falling through to mode 4's unrelated pair listing.

Definition, methodology and standing caveats for ONE stat ('ireland' or 'united-kingdom') -- never a current figure. Call this when the question is about what a metric MEANS or how it's measured ("how is the claimant count defined", "is this a mean or a median"), not about a specific area's value -- query_data/compare already answer that. stat_key comes from query_data(country=...) for the SAME country.