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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds that the scope is the full universe of stocks and that theme filtering matches on business-description text, but it says nothing about default result count, default sort direction, or what an empty result looks like.

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 purpose clause is front-loaded and the example questions at the end are high-value. The middle sentence re-enumerates all eighteen metrics, largely duplicating the `by` enum; it doubles as terminology mapping, so it is not pure waste, but it is the longest and least necessary part.

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?

For a 12-parameter screening tool with no output schema, the description never says what comes back — rank order, whether each row carries the metric value and ticker, or how many rows by default. Default behaviors (min_mcap floor for ratio rankings, ascending defaults for PER/PBR/debt) exist only inside schema field descriptions, leaving a reader of the description alone under-informed.

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 83%, so the baseline is 3, but the description earns a bump by translating opaque enum tokens into business meaning (cancel_12m = 자사주 소각 최근 1년, sh_yield = 주주환원수익률, payout = 배당성향). It also surfaces that market/industry/theme act as narrowing filters, which the bare schema does not frame.

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 verb and resource — rank all stocks ('전 종목을 한 지표로 줄 세우기') — and names the exact axis of ranking plus the narrowing dimensions (market, industry, theme). This clearly separates it from sibling lookup tools such as company, financials, and price, which return one entity rather than an ordered cross-section.

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 closing clause gives concrete trigger questions ('배당 많이 주는 회사', 'ROE 높은 회사'), which effectively tells the agent when this tool is the right choice. It stops short of naming alternatives or exclusions (e.g. 'use company for a single ticker'), so it is clear context rather than explicit routing.

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