Suomiatlas — Finnish area statistics

Rank Finnish areas by a metric

rank_areas

Rank Finnish postal areas by any metric — highest or lowest income, population, unemployment, education, housing prices — nationally or within one municipality or region. Crime rate metrics (crime_total_rate and friends) rank municipalities instead, since crime is only published at that grain. For gender_balance_20_39 the useful question is usually which areas are closest to an even split, not which are most skewed: pass order="balance" for that, since both a high and a low ratio mean imbalance. Get valid metric codes from list_variables. Free callers receive at most 10 rows regardless of limit, reported in locked. near plus withinKm narrows the ranking to areas within a straight-line radius of another place. That is distance over the ground, never a travel or commute time. To screen areas on several metrics at once — "above-average family share and below-average housing prices" — rank by one metric and pass the rest as includeVariables; that is a single call. Do not call get_place_stats per area to collect a second metric.

Input Schema

TableJSON Schema

Name	Required	Description
`near`	No	Keep only areas within `withinKm` of this place — a postal area or municipality, by name or code. Distance is centre to centre in a straight line.
`year`	No	Year; defaults to the newest with data.
`limit`	No	Rows to return (default 10).
`order`	No	Highest first (default), lowest first, or — for a metric whose ideal value is a specific number rather than an extreme — closest to that value first. "balance" is currently valid only for gender_balance_20_39, whose target is an even 1.0; asking for it on any other metric is an error.
`scope`	No	Limit to one municipality or region by name or code, e.g. "Espoo", "Uusimaa".
`withinKm`	No	Radius in kilometres around `near`. Required with `near`, and only with it.
`variableCode`	Yes	Metric code from list_variables, e.g. "hr_mtu", "unemployment_rate".
`includeVariables`	No	Extra metric codes to report for each ranked area, as `additionalValues` on every row. Each metric's own year is given in `includedVariables`, since the sources run to different years. Not available for crime rankings.

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description carries full behavioral weight — and it is unusually transparent. It discloses free-tier row caps and the locked field, crime metrics only being published at municipality grain, straight-line vs travel distance, per-metric year reporting in includedVariables, and the error case for using balance on other metrics.

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?

The description is long but information-dense: every sentence contributes a distinct constraint or behavioral rule, and the core purpose is front-loaded in the first sentence. It could be restructured into clearer bullets or sections, but it is not padded or vague.

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

Completeness4/5

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

Given 8 parameters, no output schema, and no annotations, the description covers the key edge cases: free-tier caps, crime/municipality grain, straight-line distance semantics, order errors, and multi-metric screening. It doesn't spell out the exact row shape of the response, but it names locked, additionalValues, and includedVariables, which is enough for a caller to reason about output.

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?

The schema has 100% coverage, but the description adds substantial extra meaning: when "balance" order is the right semantic, subtlety of gender imbalance being high in either direction, free-tier limits on `limit`, the real-world meaning of `near` + `withinKm`, and how `includeVariables` behaves across different data years. This is material guidance that helps an agent choose parameters correctly.

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 exactly what the tool does: rank Finnish postal areas by any metric, with concrete examples like income, population, unemployment, and housing prices. It also clarifies the scope (national, municipality, or region) and distinguishes a special case where crime metrics instead rank municipalities, so the purpose and grain are unmistakable.

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

Usage Guidelines4/5

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

It gives strong, concrete usage guidance: use order="balance" for gender_balance_20_39, use includeVariables for multi-metric screening instead of calling get_place_stats per area, and get metric codes from list_variables. It doesn't enumerate all sibling alternatives, but it names the most important one and the main multi-metric workflow explicitly.

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

All Suomiatlas — Finnish area statistics tools Inspect

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TDQS

A4.5/5.0

Disambiguation5/5

Each tool targets a unique operation: comparison, similarity, air quality, report download, history, current stats, variable listing, ranking, and place resolution. There is no functional overlap, and the descriptions reinforce distinct purposes.

Naming Consistency5/5

All tools use consistent verb_noun naming: compare_areas, find_similar_areas, get_air_quality, get_area_report, get_place_history, get_place_stats, list_variables, rank_areas, search_places. The pattern is uniform and predictable.

Tool Count5/5

With 9 tools, the set is well-scoped for a statistics server covering search, retrieval, comparison, ranking, and special features like air quality and reports. Each tool earns its place without bloat.

Completeness5/5

The tool surface covers the full workflow: resolving place names, listing available metrics, retrieving current stats, historical trends, area comparisons, ranking, air quality, similar areas, and report downloads. No obvious gaps for a read-only statistics API.

Resources

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