Geo-Parity

Compare Healthcare Costs & Exposure

compare_healthcare

Read-onlyIdempotent

Healthcare cost and financial exposure across 41 countries: out-of-pocket spend per person per year (US$), out-of-pocket as a share of all health spending, total health spend per capita (PPP international $), government funding share, physicians per 1,000 and dentists per 10,000. Figures are MEASURED from World Bank and WHO Global Health Observatory series, never derived from a price index: scaling healthcare by a country's general price level overstates costs abroad by 1.2x-5.4x, because health prices do not track the price level (the US is an outlier and several countries deliberately price healthcare below their own economies). Share and burden are separate facts and both are returned - the US pays only ~11% out of pocket, a low share, but the highest absolute amount. Dental is reported as ACCESS (dentist density), not price: no free citable source publishes procedure prices. National averages, not a quote for any individual's care. Not medical, insurance or financial advice.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`sort`	No	Metric to rank by. 'outOfPocket' = out-of-pocket spend per person per year (default, the figure a person actually pays); 'spend' = total health spend per capita; 'exposure' = out-of-pocket as a share of health spending; 'doctors' = physicians per 1,000 (ranked best-first); 'dentists' = dentists per 10,000 (best-first).	outOfPocket
`country`	No	Optional ISO 3166-1 alpha-2 country code (case-insensitive). Echoes that country's row and its rank under the active sort.
`maxOutOfPocketPct`	No	Optional filter: keep only countries where households pay at most this percentage of health spending directly.
`minPhysiciansPer1000`	No	Optional filter: keep only countries with at least this many physicians per 1,000 people.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	No
`rows`	No	Countries under the active sort, best first.
`focus`	No	The requested country's row, when `country` was given.
`updated`	No	ISO date the underlying data was last refreshed.
`excluded`	No	Countries removed by filters or missing the sorted metric.
`attribution`	No	Sources, a quotable citation and a permalink.

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the annotations, the description discloses important behavioral details: figures are measured from World Bank/WHO and never derived from a price index, share and burden are separate facts, dental is access not price, results are national averages not individual quotes, and it is not advice. This is substantial contextual disclosure that prevents misuse.

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 dense and front-loaded with the core scope and metric list, and every sentence adds useful caveats. It could be slightly shorter without losing value, but the length is justified given the tool's interpretation pitfalls.

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?

With an output schema present, the description does not need to explain return values. It covers the country scope, metric definitions, data provenance, major interpretation caveats, dental limitation, and advice disclaimer. This is complete for an agent to call the tool correctly and interpret results appropriately.

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

Parameters3/5

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

The input schema already documents all parameters with 100% coverage, including enum meanings and filter semantics. The description adds general context about metrics like out-of-pocket share and dental access, but it does not need to repeat parameter-level detail and does not substantially extend what the schema already provides.

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 offers: healthcare cost and financial exposure across 41 countries, with a precise and rich metric list. This makes the resource and scope unmistakable and clearly separates it from broader comparison tools like compare_total_cost or compare_countries.

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?

The description gives clear context for when this tool is relevant: comparing healthcare spending, out-of-pocket exposure, doctor/dentist availability across countries. It does not explicitly name alternative tools or give when-not-to-use rules, but its domain-specific scope is enough for an agent to select it over non-healthcare siblings.

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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TDQS

A3.9/5.0

Disambiguation3/5

Several tools occupy adjacent territory: check_travel_residency explicitly supersedes check_residency, check_schengen, and check_tax_residency_risk, and the cost-of-living family (arrival_cost, compare_total_cost, compare_housing, estimate_micro_costs, calculate_ppp, localize_salary) has overlapping price-level concepts. The detailed descriptions mostly clarify boundaries, but an agent selecting by name alone could easily pick the wrong residency or cost tool.

Naming Consistency4/5

Most tools follow a clean verb_noun snake_case pattern with recognizable prefixes (compare_*, check_*, find_*, calculate_*), which makes the set predictable. Deviations like arrival_cost, passport_power, required_rate, and the acronym-heavy calculate_ppp keep it from being perfectly uniform.

Tool Count3/5

26 tools is a heavy surface, and at least three of the check_* tools are explicitly superseded by check_travel_residency, adding redundant weight. The broad geo-arbitrage/nomad domain justifies much of the breadth, but the set feels closer to a full toolkit than a focused server.

Completeness4/5

The toolset covers the core remote-work and geo-arbitrage decision space: cost of living, purchasing power, tax residency risk, nomad visas, housing, healthcare, climate, connectivity, holidays, salary localization, FIRE, livability, and even team timezone planning. Gaps are minor—no family-size cost tool, no immigration/citizenship pathway data, and no dedicated safety/crime dataset beyond the livability composite—but agents can work around them.

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