top_by_median

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

With no annotations, the description carries the behavioral disclosure burden. It details sort order (highest first), substring filtering for industry, the floor logic for min_median with an example, and the clamping behavior for limit. This goes beyond the schema, though it does not cover error behavior or data source nuances.

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 extremely concise: one purpose sentence followed by a clean Args list. Every sentence earns its place, and the structure is easy to scan; no redundancy or filler.

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 tool has only three optional parameters and an output schema exists, the description is nearly complete. It covers the ranking metric, ordering, filtering options, limiting behavior, and unit interpretation. The example clarifies the min_median semantics, making the tool well-specified for an AI agent.

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 description fully compensates by explaining each parameter: industry as an optional substring filter, min_median as a floor in 萬元/yr with a concrete example, and limit with range and clamping. This adds substantial meaning beyond the bare 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 clearly states the tool ranks companies by non-managerial median pay, highest first. This specific verb+resource (rank companies) and the metric (median pay) distinguishes it from sibling tools like lookup_company and company_trend.

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 (ranking top companies) but does not explicitly state when to use this tool versus alternatives such as industry_stats or company_trend. It provides no when-not-to-use guidance or mention of alternative tools.

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