get_air_quality_forecast

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

There are no annotations, so the description carries the full burden. It discloses the model source, the city-wide scope, and the exact output shape (date, predicted AQI, category). It does not mention data freshness or caveats, but for a read-only forecast query it is reasonably transparent.

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 short sentences with no filler. The purpose is front-loaded, and the output/model context fits in the second sentence.

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 simple one-parameter read-only tool with no output schema, the description covers the geographic scope, forecast nature, model source, and returned fields. A small gap is the lack of date-format or AQI-unit details, but nothing critical is missing.

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?

The single parameter days is fully documented in the schema with default, min, max, and meaning. The description only indirectly references upcoming days and adds little beyond the schema. Baseline 3 applies.

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 identifies a specific verb (get), resource (Yerevan city-wide AQ forecast from the Predicted_AQI model), and scope (coming days). It also clearly distinguishes itself from sibling tools like get_air_quality or get_station_history through the forecast and city-wide framing.

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?

It provides clear context: use when you need predicted city-wide air quality for future days. However, it does not explicitly name sibling alternatives or state when not to use it, so some routing inference is required.

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