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

Annotations include openWorldHint:true but description does not contradict it. The description explains scope (macro indicators) and provides a pricing model (per data unit with meter details). However, it doesn't disclose behavior like version matching or mismatch handling in the description itself (though the schema param description does). No contradiction, but limited extra behavioral detail beyond annotations and schema.

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?

Description is compact two sections: scope and exclusions plus typical queries and pricing. Every sentence serves a purpose—defining indicators, excluding out-of-scope items, giving query examples, and specifying billing model. No fluff.

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?

Given the tool's moderate complexity (3 params, output schema present, open world hint), the description covers scope, exclusions, typical usage, and pricing, which is sufficient. It doesn't detail output format but the output schema exists, so that gap is acceptable. Slight additional detail on version mismatch handling is more in the schema param, but the description could mention that behavior; still, overall it's quite complete.

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% and each parameter (versions, gov_names, input_text) has detailed descriptions explaining soft constraints, default extraction behavior, and role of gov_names based on query pattern (point/compare vs rank/list/filter). The tool description adds the semantic scope (indicators covered) and examples, complementing the schema without repeating. This matches the baseline 3 for high schema coverage.

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?

Description clearly states it queries regional ecological environment macro indicators (air quality AQI/PM, good days, surface water grade, environmental governance stats). It explicitly names the resource and indicator types, and distinguishes from siblings by excluding public opinion sentiment and POI details, which maps directly to sibling tools gov_data_public_opinion and gov_data_poi_amenity.

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?

Provides explicit when-to-use guidance via typical question examples (e.g., proportion of good air quality days in a district, PM2.5, best air quality city). It also states exclusions ('not public opinion sentiment - use 舆情主题; not POI details') and gives typical query phrasing, helping differentiate from siblings.

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