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

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

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

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 1, 2026

addedInput schema / examples

Added value: +[
+  {
+    "base": "GB",
+    "target": "TH"
+  }
+]

First observedSep 1, 2026

TDQS

A3.5/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behavior, but the description adds meaningful context: the estimation formula (usPrice × pppIndex^elasticity), the Balassa-Samuelson rationale for non-tradables, and the critical caveat that these are 'model estimates, not price quotes.' It does not cover data recency or accuracy limitations, but the core behavioral traits are well disclosed.

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 purpose and efficiently packs the formula, return summary, and caveat into a compact paragraph. It is dense but has no filler; the return summary is somewhat redundant given the output schema, but it still aids quick comprehension.

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?

With annotations covering safety, a rich input schema, and an output schema, the description appropriately focuses on methodology and the 'model estimate' caveat. It is nearly complete for correct invocation, though it omits any when-to-use guidance relative to siblings.

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 parameter meaning is already fully documented. The description adds no parameter-specific details beyond the schema, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Estimates') and resource ('local prices for a 32-item everyday basket') in two countries, making the scope clear. It distinguishes itself from generic cost tools by naming the basket and the Pint & Property Index, but it does not explicitly contrast with siblings like compare_food_cost 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 Guidelines2/5

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

No when-to-use guidance or alternatives are provided. The description explains the model and output but never says when an agent should choose this tool over siblings such as compare_food_cost or calculate_ppp.

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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