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

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

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

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 1, 2026

addedInput schema / examples

Added value: +[
+  {
+    "income": 100000,
+    "target": "TH"
+  }
+]

First observedSep 1, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already establish readOnly, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description adds genuine value beyond them: it discloses the exact formulas used for each returned figure, the US baseline of 1.0, and the real-world data sources (World Bank, IMF, OECD, Penn World Table), which helps the agent trust and interpret results.

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?

One front-loaded sentence that opens with the inputs and moves to the outputs and provenance. Slightly dense with three formula definitions packed in, but every clause carries information and there is no 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?

For a read-only calculation tool with a 100%-covered schema and an output schema, the description supplies everything the agent needs: the computational model, the baseline convention, and the data sources. Any remaining return-format detail is covered by the output 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%, so the baseline is 3; the enum values, defaults, and per-parameter nuances (city slug heuristic, source dataset semantics) are all documented in the schema itself. The description only restates the two required inputs (US-denominated income, country code) and adds no syntax or selection detail beyond the schema.

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?

States a precise operation with three named outputs (effective local purchasing power as income/pppIndex, lifestyle equilibrium cost as income*pppIndex, purchasing power ratio as 1/pppIndex) and names the data provenance. An agent can distinguish this computation tool from sibling cost/salary comparison tools without opening the schema.

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?

Usage context is implied by the required inputs (US-denominated income + country code), but there is no explicit when-to-use guidance, no mention of when this is preferable to siblings like localize_salary, compare_total_cost, or compare_food_cost, and no stated prerequisites or limitations.

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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