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

Beyond the openWorldHint annotation, the description discloses meaningful behavior: it returns detail lists, excludes count aggregation, and explains the per-data-unit pricing model (region × POI type, not per row). No contradiction with annotations; the '等' in coverage aligns with openWorldHint.

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 core purpose is front-loaded in the first sentence, followed by coverage, exclusions, and examples. The pricing block is detailed but useful for cost expectations; it is not wasteful, though slightly longer than strictly necessary.

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 an output schema present and 100% parameter documentation, the description is complete enough for selection and invocation. It covers scope, exclusions, typical queries, and pricing; the only minor gap is not naming the exact count-tool sibling.

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 schema already documents all four parameters. The description reinforces the single-region constraint and gives typical query examples, but adds little parameter-level meaning beyond the schema.

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 opens with a specific verb+resource: '查询该行政区范围内的科教文化 POI 明细列表', and enumerates covered categories (schools, museums, libraries, science museums). This clearly distinguishes it from sibling POI tools by category and from count/statistics tools.

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?

It states when to use ('基于明确的市级或区县级行政区名称...典型问法') and explicitly says it does not answer count statistics, directing users to '兴趣点数量指标'. However, it does not name a specific sibling tool ID for counts, so the alternative is slightly generic.

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