get_agriculture_employment

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

With no annotations, the description carries the transparency burden. It discloses that the operation is a read-only query and specifies the return format (percentage of total employment). It also details input constraints (ISO 2/3-letter code, year >= 1960). However, it does not mention data availability, error behavior, or other operational nuances, but for a simple lookup this is largely adequate.

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 concise and well-structured: a one-sentence purpose statement followed by a clearly formatted Args section and a Returns line. Every sentence carries essential information, with no redundant or filler content.

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's simplicity (only two parameters, no nested objects) and the presence of an output schema, the description covers the core aspects: purpose, parameters, and return semantics. Minor omissions like error handling or data source details are not critical for a basic lookup, but the absence of any usage alternatives slightly reduces completeness.

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%, so the description compensates fully. It documents each parameter beyond the schema's type information: country as ISO 2- or 3-letter code with examples, and year as a four-digit year with a minimum constraint. This adds real semantic value for the agent.

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's function: 'Queries the agricultural sector employment ratio for a specific country and year.' It uses a specific verb (조회합니다/retrieves), identifies the resource (agriculture sector employment rate), and distinguishes itself from sibling tools (get_industry_employment, get_services_employment) by explicitly focusing on agriculture.

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 the use case—querying agriculture employment for a country/year—but provides no explicit guidance on when to choose this tool over siblings or when not to use it. There are no exclusion criteria or alternative tool references, so the context is only implicit.

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