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Aleva open data

Passengers per airport and year

airport_passengers
Read-onlyIdempotent

Passengers per airport and year. Returns a list of records; pass next_cursor to get the next page. Fields: year (integer); airport (varchar); passengers (bigint); movements (bigint); international_share (double); months (bigint): Months reported (the current year is partial).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoYear (equals)
limitNoHow many rows to return
cursorNonext_cursor from the previous result, for the next page
airportNoAirport (as reported, upper case) (equals)
year_gteNoYear (at least)
year_lteNoYear (at most)
airport_prefixNoAirport (as reported, upper case) (starts with)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and closed-world, so safety is covered. The description adds genuinely new context beyond them: results come back as a list, pagination works by passing next_cursor, and the caveat that months is partial for the current year.

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 purpose, then pagination, then a compact field list. The trailing field enumeration is dense but each entry earns its place given no output schema. No filler sentences.

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 no output schema, the description correctly takes on the burden of describing return fields and pagination, including the partial-year caveat for months. An agent has enough to call and interpret the tool, though return-type/format details for a single page are only lightly specified.

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 coverage is 100%, so every parameter (year, limit, cursor, airport, year_gte, year_lte, airport_prefix) is already documented in the schema. The description does not add filter semantics beyond the schema; its field list describes return columns, not parameters, so the baseline 3 applies.

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?

States a specific resource and scope ('Passengers per airport and year') and enumerates the returned fields, so an agent knows this queries passenger statistics. It does not explicitly distinguish itself from the sibling air_passengers_monthly, which is the main gap preventing a 5.

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, no alternatives named, and no explanation of how the year/airport filters interact. The only operational hint is pagination via next_cursor, which is a mechanics note rather than usage guidance.

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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