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

Annotations already declare the tool read-only and idempotent. The description adds useful behavioral context beyond that: data source (Open-Meteo/CAMS), geographic scope (US & European AQI), cost per call, and the underlying endpoint. It does not describe response format limitations, but annotations lower the burden here.

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?

The description is compact: one sentence for what it returns, one for source, and one for cost/endpoint. Every piece adds useful context without redundancy or filler.

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 low-complexity lookup tool, the description covers what the tool returns, how it is invoked (by coordinate), its data source, and its cost. It does not explicitly describe the output schema or clarify whether q vs lat/lon is preferred, but the schema covers those details sufficiently.

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 100%, so the parameters are already well documented with types, descriptions, and examples. The tool description adds only the general notion of coordinate-based lookup and does not need to explain each parameter further.

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?

The description clearly identifies the resource as air quality and specifies the data returned: US & European AQI plus pollutant concentrations, with coordinate-based lookup. It is clear and unambiguous, though it lacks an explicit verb and does not differentiate from neighboring tools like v1_weather.

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?

The description gives clear context by stating that this is a coordinate-based air quality lookup, so an agent can infer when to call it. It does not explicitly mention alternatives or exclusions, which prevents a 5.

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