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What a job pays in a place

check_salary
Read-onlyIdempotent

What a job pays in a place. For "what does a nurse earn in Birmingham", "average salary for a data analyst in Austin", "is £35k good for a junior developer in London". Gives the typical yearly range (middle half of current adverts), the middle figure and the average advertised, from current job adverts. Advertised pay, not offers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whatYesJob title or field, for example "nurse" or "software engineer".
whereNoA city, region, postcode or country, for example Birmingham, Austin, TX or remote.
countryNoUS or UK when the place exists in both, or a country by name.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive. The description adds genuine context beyond them: the data source (current job adverts), the shape of the result (typical yearly range = middle half of adverts, the middle figure, the average advertised), and the important caveat "Advertised pay, not offers." Remaining gap: no note on data freshness or geography/comparability limits.

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?

Front-loaded with the core purpose, then examples, then the return shape and caveat. Every sentence carries information, though three examples is one more than strictly needed; the punchy "Advertised pay, not offers" earns its place.

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?

With no output schema, the description carries the burden of describing returns and does so (range, middle figure, average advertised), plus the data source and the advertised-vs-offered caveat. An agent has everything needed to call and interpret this read-only lookup.

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 every parameter is already documented and the baseline is 3. The description's examples implicitly illustrate how `what` and `where` combine, but adds no syntax or semantics for `country` beyond what the schema states.

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 specific verb+resource (check what a job pays) with scope (a job in a place), and none of the siblings cover salary lookup, so it is unambiguous with no competitor. The title reinforces the same specific purpose.

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

Usage Guidelines4/5

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

Three example phrasings ("what does a nurse earn in Birmingham", "average salary for a data analyst in Austin", "is £35k good for a junior developer in London") clearly delimit the query types it answers. It doesn't explicitly name an alternative tool or say when NOT to use it, but no sibling overlaps this space.

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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