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InfraNode

by dev.infranode

Air Quality

air_quality
Read-onlyIdempotent

Get official air quality for a German city (PM10, NO2 and more).

Sourced from the Umweltbundesamt (UBA). Read-only. For live nearest-station hourly readings use get_city_resource(slug, resource='air') instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesCity identifier, e.g. 'berlin' or 'hamburg'. Resolved leniently: the German name with or without umlauts, any casing, a common English exonym or short form also works (München/munich/munchen -> muenchen, cologne -> koeln, frankfurt -> frankfurt-am-main). An unknown name returns 404 with a 'Meintest du ...?' suggestion. list_cities gives the canonical slugs.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
metaYes

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds the specific data source (UBA), which provides source transparency. No additional behavioral details (e.g., rate limits) are given, but the annotation coverage is strong.

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?

Two concise sentences: first states purpose and pollutants; second provides source and alternative tool. Front-loaded with the main action, no filler.

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?

For a simple one-parameter tool with a rich schema and output schema, the description covers the data source, scope, and key data returned. It also references the sibling for different use cases, making it self-contained.

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%, with detailed resolution logic for the slug parameter. The tool description does not add parameter-level details beyond the schema, so baseline 3 applies.

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 states the action ('Get'), the resource ('official air quality'), the scope ('German city'), and specific pollutants ('PM10, NO2 and more'). It distinguishes this tool from the sibling `get_city_resource` by contrasting data source (UBA vs. live nearest-station hourly readings).

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

Usage Guidelines5/5

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

Explicitly states when to use (for official city air quality) and when not, providing the exact alternative call: `get_city_resource(slug, resource='air')` for live hourly readings. Also notes the data source (Umweltbundesamt).

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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TDQS

A4.1/5.0
Disambiguation3/5

Most tools have distinct purposes, but there is potential confusion between 'get_city_resource' and specific resource accessors like 'weather', 'air_quality', 'transit_departures', 'station_board_arrivals', and 'station_board_departures'. The descriptions do clarify that some specific tools exist for convenience or source-specific details, but the boundary isn't always sharp. Also, 'compare' could overlap with using 'get_city_resource' repeatedly. An agent might hesitate between using the generic resource accessor and the named tool.

Naming Consistency4/5

The naming convention is mostly consistent: verbs like 'get', 'list', 'compare', and 'pois' (abbreviation) are used. The pattern is generally 'verb_noun' (e.g., 'get_city', 'list_cities', 'station_board_arrivals'). Minor deviations include 'pois' being an acronym rather than a full verb phrase, and 'air_quality' vs 'weather' implying a noun rather than an action. But overall it's predictable and readable.

Tool Count5/5

12 tools is a well-scoped count for a city data platform. The function set covers discovery (list_cities, sources), overview (get_city_overview), base data (get_city), specific data types (weather, air_quality, transit_departures, station_board_*, pois), a generic accessor (get_city_resource), and a comparison tool (compare). Each tool feels necessary and the set is not overwhelming.

Completeness4/5

The tool surface is quite comprehensive for a read-only city information server. It provides discovery (list_cities, get_city_overview), base data, and access to 81 data types via get_city_resource. The named tools cover the most common queries (weather, air quality, transit). A minor gap is the lack of a tool to aggregate or search across cities (though 'compare' helps). Editing or write operations are not expected here, but for read-only, it's nearly complete.

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