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MCPFax Public-Data Utility API

Nearest airports

v1_airports_nearest
Read-onlyIdempotent

Nearest airports: Coordinates → nearest airports (IATA/ICAO) by distance. Source: OurAirports (edge dataset). $0.008 per call · GET /v1/airports/nearest

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude. Example: '40.71'.
lonYesLongitude. Example: '-74.0'.
limitNoMax results 1-25. Example: '5'.
radiusNoSearch radius km (default 500). Example: '300'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / lat / examples
      Added value: +[
      +  40.71
      +]
    • addedInput schema / properties / limit / examples
      Added value: +[
      +  5
      +]
    • addedInput schema / properties / lon / examples
      Added value: +[
      +  -74
      +]
    • addedInput schema / properties / radius / examples
      Added value: +[
      +  300
      +]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond that: it discloses pricing per call, the data source (OurAirports), that it is an 'edge dataset', and that results include IATA/ICAO codes by distance. This goes beyond the minimum and helps set expectations.

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 a single compact sentence that front-loads the core purpose and then adds high-value metadata (endpoint, source, price). There is no filler or repetition of schema content.

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?

For a simple four-parameter lookup with rich annotations, the description covers the essential context: purpose, input mapping, output identifiers, data source, and cost. There is no output schema, so a little more detail about the exact response shape could help, but the description gives enough for correct selection and invocation.

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 in the input schema. The description does not add parameter-specific guidance, but it does not need to because the schema provides clear examples and defaults. Baseline 3 is appropriate.

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 clearly defines the operation as converting coordinates into the nearest airports by distance, with a specific resource ('airports') and endpoint path. It distinguishes this tool from siblings by naming the exact functionality and even notes IATA/ICAO output. The arrow notation is a clear functional mapping, so the purpose is unambiguous.

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?

The description clearly implies this tool should be used when coordinates are available and the nearest airports are needed. It does not explicitly state when not to use it or point to alternatives like reverse geocoding, but the context is strong enough for an agent to select it correctly.

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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