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

With no annotations, the description carries the full burden and does disclose real behavior: the response always includes population size and field coverage, and sorting on performance fields is prohibited for a policy reason (no product recommendation). It does not cover auth, rate limits, or error behavior, but the key output-shape and constraint traits are surfaced.

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?

Content is front-loaded: purpose first, then where syntax, then response guarantees, then the sort caveat. The bilingual restatement at the end is mildly redundant but short, and every other sentence carries information.

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?

No output schema exists, and the description compensates by stating that responses always carry population and coverage. Combined with the aggregate function list and where grammar, an agent has enough to call it correctly; only pagination/limit behavior is left entirely to the schema.

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 baseline is 3; the description adds genuine value by explaining the where grammar ('欄位 運算子 值' joined by and), listing the aggregate functions available, and giving the rationale behind the sort restriction rather than merely restating it.

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?

States a specific two-part verb (filter a universe + aggregate over the matched set) with a concrete worked example ('費用率 0.4% 以下的被動 ETF，其費用率中位數') that makes the group-level nature unmistakable. This distinguishes it from single-record siblings like data_point and row-returning ones like data_table.

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 example conveys the intended usage context, and the sort restriction ('績效欄位…不得作為排序鍵——排序即排名') gives a clear do-not rule for one parameter. However, it never says when to pick this over data_point, data_series, or data_table, leaving the sibling routing to inference.

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