Geo-Parity

Shared working days and public holidays by country

compare_public_holidays

Read-onlyIdempotent

Two questions in one. Given countries (two or more), returns the working days those places genuinely SHARE in a year — weekdays on which nobody is on a public holiday — which is the number a distributed team actually plans against and the half of the question timezone-overlap tools never answer: a London/Tokyo team loses roughly three working weeks a year to non-overlapping holidays. Without countries, ranks countries by nationwide public holidays. Weekend holidays are excluded (they cost nobody a working day) and regional holidays are counted separately from national ones. Covers 204 countries, but NOT India, Thailand, Malaysia, the UAE or Saudi Arabia — those return NO_DATA rather than an empty calendar, because “no data” and “no holidays” are opposite claims.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`sort`	No	Ranking direction. Ignored when `countries` is given.	most
`year`	No	Calendar year. Defaults to the first year in the dataset.
`limit`	No	Return at most this many countries in a ranking.
`countries`	No	Comma-separated ISO alpha-2 codes, two or more (e.g. "GB,JP"). Omit to get a ranking instead of an overlap.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ok`	No
`rows`	No
`year`	No
`lostDays`	No
`weekdays`	No
`countries`	No
`lostDates`	No
`sharedWorkingDays`	No

TDQS

A4.6/5.0

Behavior5/5

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

Even with readOnlyHint and idempotentHint annotations, the description adds meaningful behavioral nuance: weekend holidays are excluded, regional holidays are counted separately from national ones, and five named countries return NO_DATA instead of an empty calendar to avoid conflating no data with no holidays. This is exactly the kind of non-obvious result behavior an agent needs.

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 front-loaded with the core two-mode behavior and each sentence contributes either a usage rule, an output interpretation, or a coverage caveat. It is a bit long with the London/Tokyo example, but the extra detail is substantive rather than 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?

With an output schema present and annotations covering safety, the description still covers the critical contextual gaps: conditional parameter behavior, holiday counting methodology, and geographic coverage exceptions. An agent can call this tool confidently without hidden surprises about modes or missing-data semantics.

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 100%, so the baseline is 3, but the description adds real value by explaining the dual-mode semantics of countries and explicitly noting that sort is ignored when countries is supplied. It also clarifies how weekend and regional holidays affect the calculation, which is not evident from the schema alone.

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 opens with 'Two questions in one' and clearly defines both modes: given two or more countries it returns shared working days, otherwise it ranks countries by public holidays. This is a specific verb-plus-resource statement that distinguishes the tool from the many compare_* siblings.

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 explicitly ties usage to the presence of the countries parameter: provide it for overlap, omit it for ranking. It also contrasts the tool with timezone-overlap tools, explaining the gap it fills. It does not name a sibling alternative directly, but the conditional guidance is clear and actionable.

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

All Geo-Parity tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?