air_quality_hist

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds valuable context about performance (hourly period downloads slowly due to large data size), the data source URL, and return format, which goes beyond the annotations. No contradictions found.

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 somewhat dense and mixes prose with docstring-style param/type/return blocks, but is generally clear. It includes a URL and period guidance that are useful, though the formatting is not ideal for quick scanning and is slightly verbose.

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?

With no output schema, the description still explains the return type (pandas.DataFrame) and the date range semantics. It covers all 4 parameters, data source, and performance caveats. For a read-only, idempotent tool, this is reasonably complete, though it doesn't specify units or exact columns of the returned DataFrame.

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 carries full responsibility for parameter meanings. It explains every parameter (city, period, start_date, end_date) with types and concrete examples (e.g., '20190327'), and even clarifies the return type. Despite lacking formal schema descriptions, the description compensates well.

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 this is a historical air quality data retrieval tool from 真气网 (zq12369.com), with a specific resource (air quality by city, period, and date range) and a defined return type (pandas.DataFrame). It's distinguishable from siblings like air_quality_hebei or air_quality_rank by focusing on historical time-series data for any city, though it doesn't explicitly contrast with them.

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 provides explicit usage instructions for all parameters, including examples and guidance on period selection (e.g., 'hour' downloads slower due to data volume). It references using ak.air_city_table() to get the city list, but does not explicitly state when to use this tool versus alternatives, though the historical nature is implied.

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