get_air_quality

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

With no annotations, the description carries the full disclosure burden and delivers the non-obvious part: values are modelled estimates, not ground-station readings, which tells the agent how much to trust the numbers. It omits any statement on rate limits or behavior for an unrecognized city, so it is not fully complete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the verb, resource and return payload, followed by the caveat. Nothing redundant, nothing padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter read tool with no output schema, the description covers what comes back (AQI plus pollutant levels) and the data's reliability. The one gap is the lack of differentiation from aqi_by_pincode, which is exactly the disambiguation an agent needs from the sibling list.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does by giving concrete example values ('Bengaluru', 'Delhi') that establish the expected naming convention. It doesn't specify whether variants like 'Bangalore' or 'New Delhi' are accepted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (get current air quality), what is returned (AQI and pollutant levels), and the geographic scope (an Indian city). It never mentions the sibling aqi_by_pincode, so the city-vs-pincode distinction must be inferred from the parameter rather than stated.

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

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

Usage is only implied: the examples 'Bengaluru' or 'Delhi' show this tool takes a city name, which hints that aqi_by_pincode handles postal-code lookups. There is no explicit 'use this when / use X instead' routing, which matters given a closely related sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.