data_go_kr_check_business_status

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

With no annotations, the description alone must disclose behavioral traits. It indicates a read operation but omits details on error handling (e.g., invalid numbers), rate limits, authentication needs, or whether the operation is idempotent. This lack of transparency could lead to confusion during execution.

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—two sentences and an example—with no redundant words. The key constraint (format, limit) and usage pattern are front-loaded, earning every sentence its place.

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 simple one-parameter tool without an output schema, the description covers input format and batch limit adequately. It lacks details about the return value or status, but given the tool's straightforward nature (batch status check), it is mostly complete. Minor gap: no mention of what '상태' (status) entails.

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?

The input schema has 0% description coverage, so the description must compensate. It adds significant semantic value: explains the expected number format (10 digits, no hyphen), batch limit (100), and provides an example. This goes well beyond the bare schema definition.

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 identifies the specific verb '조회한다' (inquire/check) and the resource 'business registration numbers', with precise constraints: 10 digits, no hyphen, max 100 batch. This distinguishes it from sibling tools which focus on different Korean data sources (dart, ecos, kosis, etc.).

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 explicitly states the required input format (10 digits without hyphen) and batch size limit (100), which are key usage guidelines. While it does not mention when not to use (there are no direct alternatives), the provided constraints are clear and actionable for an AI agent.

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