Geo-Parity

Calculate Purchasing Power Parity

calculate_ppp

Read-onlyIdempotent

Given a US-denominated annual income and a target country code, returns the effective local purchasing power (income / pppIndex), the lifestyle equilibrium cost (income * pppIndex), and the purchasing power ratio (1 / pppIndex). Figures use real PPP data (World Bank, IMF, OECD, Penn World Table) relative to a US baseline of 1.0.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`city`	No	Optional city slug (e.g. 'london', 'san-francisco') for a sub-national Capital-to-Rural adjustment: multiplies the national price level by a per-city factor (curated overrides / US-metro BEA RPP / population-tier heuristic). Unknown slug = national figure.
`income`	Yes	Nominal annual income, denominated against the US baseline.
`source`	No	PPP dataset to draw figures from. 'consensus' = per-country median across the GDP-based providers (recommended default); 'worldbank' = World Bank GDP price level; 'imf' = IMF WEO implied PPP; 'oecd' = OECD PPP-for-GDP (members + key partners only); 'pwt' = Penn World Table; 'worldbank-cons' = World Bank household-consumption price level; 'bigmac' = The Economist Big Mac index (single-good consumer PPP, ~40 countries); 'factbook' = CIA World Factbook derived price level for ~100 micro-states/territories the macro sources ignore (coarse). Defaults to 'consensus' if omitted.	consensus
`target`	Yes	ISO 3166-1 alpha-2 target country code (case-insensitive). Available codes depend on the chosen source.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ok`	No
`source`	No
`target`	No
`metrics`	No

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context by disclosing the calculation formulas, the data providers (World Bank, IMF, OECD, Penn World Table), and the US baseline of 1.0. It does not contradict the annotations.

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

Conciseness5/5

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

The description is two focused sentences. The first sentence front-loads the input conditions and exact outputs; the second sentence adds data provenance and baseline context. There is no filler or redundant restating of the title.

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?

Given a rich input schema (100% parameter coverage), the presence of an output schema, and annotations covering read-only/idempotent behavior, the description is complete enough. It covers the core computation, inputs, outputs, and data provenance, while edge details like source defaults and city behavior are already captured in the schema.

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%, with each parameter already explained in detail. The description adds value by clarifying how income and pppIndex relate in the formulas, but it does not add significant semantics for the target or source parameters beyond 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 a specific verb ('returns') applied to a clear resource (purchasing power parity calculations), lists the exact computed outputs with formulas, and specifies the input conditions (US-denominated annual income + target country code). This clearly distinguishes it from siblings like localize_salary or compare_total_cost.

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 description implies when to use the tool: when you have a US-denominated annual income and want purchasing-power-adjusted local figures for a country. However, it does not explicitly state when not to use it or name any alternative sibling tools, leaving the agent to infer routing from context.

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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