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 covered. The description adds that it returns summaries (id / 中文名 / category / tier / one-line description) and frames it as a discovery entry point, but it doesn't disclose behaviors such as pagination, ordering, locale, or data volume. With annotations in place, the added context is useful but not rich, meriting a 3.

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 tightly writtgen: one sentence for purpose, one for return fields, one for usage, followed by a compact Args block. Every sentence contributes a distinct piece of information and the args are formatted clearly with examples. There is no fluff or redundancy.

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 low complexity (2 optional params, no nested objects), the availability of an output schema, and annotations that cover safety/idempotence, the description covers purpose, returned fields, parameter semantics, and usage sequence. It is complete enough for an agent to invoke it correctly without further inference.

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% and the schema only shows two nullable string parameters with defaults, so the description bears the full burden of explaining them. It does so excellently: for category it lists concrete example values with Chinese labels, and for tier it explains the 'optional minimum plan' and lists valid plan names. This gives an agent exactly what it needs to construct valid filter arguments, far beyond the raw 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 states a specific verb and resource: 'List available Taiwan-market datasets' and explicitly lists the returned fields (id / 中文名 / category / tier / one-line description). The phrase 'discovery entry point' and 'Use this first' clearly sets it apart from siblings like describe_dataset or query_dataset, so an agent can distinguish it immediately.

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 context, calling the tool the 'discovery entry point' and instructing agents to 'Use this first to find the right data'. This tells when to use it, but it does not explicitly enumerate alternatives or say when not to use it. It also doesn't name sibling tools like describe_dataset or try_sample as follow-up options, so it falls just short of a 5.

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