air_city_table

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the source URL and return type, but no additional behavioral traits such as data freshness, rate limits, or dependencies. Given the annotations, a score of 3 is appropriate—it adds some value but not rich context.

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 extremely concise: a single purpose line, a source URL, and return type information. It is front-loaded, contains no fluff, and every element serves a 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?

With no output schema, the description gives only a high-level return type ('城市映射' as pandas.DataFrame). It does not detail the structure of the mapping (e.g., city names/IDs) or explain how this tool supports sibling air quality tools. For a simple no-param read-only tool, it is adequate but leaves gaps in contextual linkage.

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 tool has zero parameters, so the schema coverage is trivially 100% and there is nothing to explain. The description clarifies the return as a city mapping DataFrame, which is useful. Baseline 4 applies for zero-param tools.

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 '真气网-空气质量历史数据查询-全部城市列表' (Air quality historical data query - all city list) and specifies the return as a city mapping (城市映射) in a pandas DataFrame. This distinguishes it from sibling tools like air_quality_hist and air_quality_rank, which handle historical data and rankings respectively.

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 no guidance on when to use this tool vs. alternatives. It does not mention that this tool provides the city list needed for other air quality tools (e.g., to get city codes before querying historical data). The usage context is entirely absent.

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