StatsMapped Public Data

list_areas

Use when you need the ids and names of the areas (counties, local authorities, districts) to pass to query_data.

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`), `name` and `level`. Slim by default;
`include_parents=True` adds each area's `parents` (the regions it sits in)
and Irish-language name.

Input Schema

Name	Required	Default
`level`	No	county
`country`	No	ireland
`include_parents`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 23, 2026

addedInput schema / properties / include_parents

Added value: +{
+  "default": false,
+  "title": "Include Parents",
+  "type": "boolean"
+}

First observedOct 9, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses defaults (level='county', country='ireland'), the slim-by-default return shape, and exactly what `include_parents=True` adds. It doesn't cover error behavior or whether unknown levels raise or return empty, which is the remaining gap.

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

Conciseness4/5

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

Front-loaded with the use case and tightly scoped, with every paragraph carrying distinct information. It is slightly dense with parenthetical asides (multiple level examples, Irish-language note) that could be trimmed, but nothing is filler.

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?

For a 3-optional-param lookup tool with an output schema, the description covers trigger, scope, defaults, level semantics across both countries, and the shape of the result set. Nothing an agent needs to invoke it correctly is missing.

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

Parameters4/5

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

Schema coverage is 0% and the schema has no enums, so the description must compensate and largely does: it names the valid countries ('ireland' or 'united-kingdom'), gives the default and UK-specific meaning of `level`, and explains `include_parents`. It stops short of enumerating valid levels, deferring to `compatible_levels` instead.

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?

States a specific verb+resource (list areas at a boundary level for a country) and frames the purpose in terms of what the output feeds into: 'ids and names ... to pass to `query_data`'. An agent can distinguish list_areas from query_data/compare/explain_metric without opening any schema.

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?

Opens with an explicit 'Use when you need...' trigger and points to the alternative path for level discovery: 'see a dataset's own `compatible_levels` from `query_data`'. It also handles the country/level selection edge case (UK's primary level is 'lad', not 'county').

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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