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

With only openWorldHint in annotations, the description adds meaningful behavioral boundaries: it will not answer counts/densities, requires an explicit single region, and clarifies the billing unit (per region × POI type, not per row). It does not contradict 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 front-loaded with the core purpose, followed by coverage, exclusions, examples, and pricing. Every sentence contributes useful selection or invocation information, with no redundant restatement of the tool name or schema.

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 output schema exists and annotations are minimal, the description sufficiently covers scope, boundaries, typical queries, region constraint, POI subtypes, and cost model. It is complete enough for an agent to decide when to invoke this tool and what to pass.

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 coverage is 100%, so the baseline is 3, but the description adds value by constraining input_text to a single region, explaining that poi_type is limited to the dining candidate set, and giving concrete examples like '武侯区的星巴克分布'.

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 uses a specific verb ('查询') and resource ('餐饮 POI 明细列表') with an explicit scope: administrative region and store name/coordinates. It also clearly distinguishes itself from count/density tools and other poi_data_* category siblings by listing covered dining subtypes.

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 gives clear when-to-use context with typical question examples and explicitly excludes count/density questions. However, the suggested alternative ('兴趣点数量指标') is a concept rather than a specific sibling tool name, so the cross-tool guidance is slightly less actionable than ideal.

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