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Localize a salary for fair pay across borders

localize_salary
Read-onlyIdempotent

Convert a base salary from a home country into a cost-of-living-fair local equivalent: S_local = S_base × (I_target / I_home), where I is a US-relative price level. Omit target to receive a localized salary matrix across every country in the source — useful for pricing one offer across many markets. Returns USD purchasing-power equivalents, not currency-converted amounts.

Input Schema

NameRequiredDescriptionDefault
baseYesBase annual salary in the home country (USD-denominated).
homeYesISO 3166-1 alpha-2 home country code the base salary is expressed in (case-insensitive).
sourceNoPPP dataset to draw figures from. Defaults to 'consensus' if omitted.consensus
targetNoOptional ISO 3166-1 alpha-2 target country code. Omit to localize across all countries in the source.

Output Schema

NameRequiredDescriptionDefault
okNo
homeNo
inputNo
sourceNo
resultsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "base": 150000,
      +    "home": "US",
      +    "source": "consensus",
      +    "target": "ES"
      +  }
      +]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds genuinely useful behavior beyond that: it discloses that results are USD purchasing-power equivalents rather than currency-converted amounts, and explains the matrix behavior when target is omitted.

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?

Three sentences, front-loaded with the core action and formula, then the omit-target behavior, then the output-disambiguation caveat. Every sentence earns its place with no redundancy.

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?

An output schema exists and annotations cover safety, so return values and side effects need not be spelled out. Parameters are fully documented in the schema and enriched by the formula. Nothing an agent needs in order to call this correctly is missing.

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. The description goes further by embedding the parameters in the formula S_local = S_base × (I_target / I_home), which conveys the relational meaning of base, home and target that the schema alone does not express.

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 gives a precise verb+resource+scope ('Convert a base salary from a home country into a cost-of-living-fair local equivalent') and even supplies the exact formula, which strongly signals what the tool is. It does not explicitly name or contrast with a sibling such as calculate_ppp or compare_occupation_pay, so it falls just short of the top tier.

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?

It gives clear context for one mode ('Omit target to receive a localized salary matrix... useful for pricing one offer across many markets'), which is more than implied. However, it never says when NOT to use it or how it differs from closely related siblings like calculate_ppp, leaving the routing decision to inference.

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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