air_quality_hebei

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it specifies the return type as pandas.DataFrame and describes the filtering behavior for city (empty string returns all regions, a specific city name returns that city's data). It also includes the source URL, providing additional context beyond the annotations.

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 relatively concise, with the core purpose stated in the first line and additional return details in two lines. The inclusion of a raw URL adds some clutter but is not excessive. It is front-loaded with the tool's name and purpose.

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?

The tool has no output schema and the description only states the return type (pandas.DataFrame) but not the columns, units, or content of the returned forecast data. The city parameter inconsistency also leaves the expected inputs ambiguous. While the core purpose is clear, the description lacks sufficient detail for full self-contained understanding.

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 input schema has no properties, yet the description mentions a 'city' parameter (city = '', city='唐山市') in the return section, implying the tool accepts an argument that the schema does not define. This conflicts with the structured definition and could mislead the agent into passing a parameter that fails validation. The baseline for 0 params is 4, but the misleading parameter reference reduces the score.

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 states the tool provides Hebei Province air quality forecasts for the next 6 days, distinguishing it from sibling tools like air_quality_hist and air_quality_rank. It identifies a specific resource (Hebei air quality forecast system) and time horizon, making the purpose explicit and unique.

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 implies the tool is for forecast data (空气质量预报) but does not explicitly state when to use it over alternatives or mention exclusions. Sibling tools are not referenced, so usage guidance is only implied, not explicit.

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