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

With no annotations, the description carries the behavioral burden and does add meaningful context: data is '每日更新' (daily updated), computed by FinLab with the finlab package, and represents '真實數據' rather than simulated values. It also spells out the content dimensions returned. It does not, however, mention error behavior or symbol availability, leaving some behavioral gaps.

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 definition is a single dense but structured sentence, front-loading the action and resource before detailing returned content and ending with a clarifying example. No filler or redundancy—every clause contributes.

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?

Because there is no output schema, the description compensates by naming the concrete data categories returned (fundamental percentiles, triggered signals, forward-return statistics, valuation level). It also covers the query example and data source. It omits an exact response structure and limitations for the optional 'us' market, but is adequate for a 2-parameter read tool.

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 100%, so the baseline is 3. The description repeats the symbol='2330' example already present in the schema and does not add new meaning for the market parameter or its interaction with symbol; therefore it stays at the baseline.

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?

Uses the verb '查詢' (query) against a specific resource: daily-updated data evidence for Taiwan stocks. The description enumerates the contained data categories (fundamental percentile, signals, forward returns) and gives a concrete symbol example, clearly differentiating it from the data-catalog and documentation sibling 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?

The description implies usage by showing a symbol example and describing the output content, but it never explicitly says when to prefer this tool over siblings like get_data_catalog or get_strategy, nor states any exclusion criteria. Thus usage context is clear but not formally contrasted with alternatives.

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