StatsMapped: Irish statistics (CSO, county & council data)

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compare

Compare Irish statistics across counties or councils: rank areas by a stat, check if two stats are comparable, or get details on a vetted comparison pair.

Instructions

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.

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.
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.
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".
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.

Input Schema

TableJSON Schema

Name	Required	Default
`level`	No
`country`	No	ireland
`pair_key`	No
`stat_key`	No
`stat_key_a`	No
`stat_key_b`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.3.0Sep 26, 2026

TDQS

A5/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden and does so extensively. It discloses error-vs-empty-list behavior, the meaning of `comparable` values including how to treat `unknown`, the registry-backed limitation that never computes fresh correlations, the `rate_per_1000` vs `latest_value` distinction, and the requirement to carry forward caveats. This is far beyond typical descriptions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but exceptionally well-structured, using numbered modes and front-loading the unifying concept before detailing each mode. Every sentence provides operational guidance, and the clear mode-by-mode organization makes the length justified by the tool's genuine complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully covers the tool's decision space: four modes, parameter combinations, error conditions, output semantics (correlation stats, comparability values, caveats, rate/raw distinction), and data provenance. With an output schema present, the description is complete enough that an agent can invoke the tool correctly without needing to infer anything about behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate. It explains each parameter's role and valid combinations: `stat_key` alone for mode 1, `pair_key` alone for mode 2, both `stat_key_a`/`stat_key_b` for mode 3, and none for mode 4, plus the conditional meaning of `level`. It also gives source constraints for parameter values, making every parameter semantically meaningful.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's core purpose immediately: 'how does this stat compare' at different scopes, and explicitly maps the four modes to the four tools it consolidates. Each mode is described with a specific verb and resource (rank areas, get pair detail, check comparability, list pairs), making it easy to disambiguate from siblings and between modes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance for each mode, clarifies that exactly one mode's arguments should be given, and explains error behavior for mixing modes or providing `level` without `stat_key`. It also names where valid keys come from (`query_data`'s dataset-listing mode) and reinforces the country consistency requirement, leaving no ambiguity about selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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