Geo-Parity

Estimate everyday prices in two countries (Pint & Property Index)

estimate_micro_costs

Read-onlyIdempotent

Estimates local prices for a 32-item everyday basket (rent, pint of beer, groceries, transport, gym, …) in two countries, derived as usPrice × pppIndex^elasticity — non-tradables like rent swing harder across borders than world-priced goods like petrol (Balassa-Samuelson-style model). Returns per-item prices in USD and local currency, category subtotals, and a single-person monthly budget rollup. Model estimates, not price quotes.

Input Schema

TableJSON Schema

Name	Required	Description
`base`	Yes	ISO 3166-1 alpha-2 country code to compare FROM (case-insensitive).
`source`	No	Optional dataset override. Defaults to the consumption basket (worldbank-cons) when it covers both countries, else consensus; the response echoes the source used.
`target`	Yes	ISO 3166-1 alpha-2 country code to compare TO (must differ from base).
`baseCity`	No	Optional city slug for the base side (sub-national cost adjustment, e.g. 'london'). Enables intra-country city comparisons like London vs Hull.
`targetCity`	No	Optional city slug for the target side (e.g. 'hull').

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ok`	No
`items`	No
`source`	No
`monthlyBudget`	No
`categoryTotals`	No

TDQS

A4.1/5.0

Behavior5/5

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

Annotations already signal read-only/idempotent behavior, and the description adds meaningful transparency: the derivation formula, the distinction between tradable and non-tradable goods, the output structure (per-item prices, subtotals, monthly budget rollup), and the explicit limitation that these are estimates, not quotes.

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 a single dense paragraph with no filler; every sentence contributes methodology, scope, or output details. It could be slightly tighter, but it is well-structured and front-loaded with the primary purpose.

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?

Combined with the very detailed schema and presence of an output schema, the description covers the model, scope, outputs, and caveats. Minor gaps remain, such as explicitly explaining optional city sub-adjustments in the description itself, but the schema already handles those.

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?

Schema description coverage is 100% and each parameter already has a clear explanation. The tool description does not add parameter-specific meaning beyond generally referencing 'two countries' and the basket contents, so the baseline score of 3 is appropriate.

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 uses a specific verb ('Estimates') with a concrete resource ('local prices for a 32-item everyday basket in two countries') and clearly differentiates this from siblings via the Balassa-Samuelson-style model and the 'Model estimates, not price quotes' caveat.

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

Usage Guidelines3/5

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

The intended use case is implied clearly—estimate everyday consumer prices across two countries—but the description does not explicitly state when to choose this over alternatives like compare_total_cost or calculate_ppp, nor does it frame exclusions beyond the 'not price quotes' caveat.

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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