Korea Business Verify (KBV)

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

Even though annotations already declare readOnlyHint/idempotentHint/openWorldHint, the description adds genuinely valuable behavioral context: results may be served from a cache up to 24 hours old and are marked 'cache': true, order is preserved, a summary count is returned, and the data source is Korea National Tax Service. The cache-staleness disclosure is exactly the kind of gotcha that annotations cannot express.

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?

Four sentences, each earning its place: purpose and limit first, then return shape, then usage routing, then cache behavior and data source. No filler or repetition of schema 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?

With a single parameter, 100% schema coverage, an output schema present, and safety annotations already supplied, the description covers all remaining decision-relevant context: batch limit, cache semantics, ordering guarantees, and data provenance. Nothing an agent needs to select and invoke this tool correctly is missing.

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% — the schema already documents the array type, 1-100 count, and that hyphens/spaces are allowed, so baseline is 3. The description adds the '10-digit' format constraint and the Korean term 사업자등록번호, which help an agent validate inputs beyond what the schema states.

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 states a specific verb ('Check'), a concrete resource ('registration status ... of Korean businesses'), and a clear scope ('up to 100 ... in a single call'). It also names the key identifier format (10-digit 사업자등록번호), which makes the tool's function unmistakable and distinct from a single-check tool.

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 gives explicit routing guidance: 'Use this instead of repeated single checks when screening supplier or customer lists.' This clearly selects this tool over the single-check sibling check_korean_business_status. However, it does not state when not to use it or how it relates to the third sibling, verify_korean_business, so exclusions are incomplete.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds meaningful context by naming the Korea National Tax Service as the data source and noting that it is real-time, which signals live external data and possible variability beyond static or cached sources.

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 three sentences long, front-loaded with the core purpose, and every sentence earns its place: input format, output meaning, and data source. There is no filler or redundant restatement of the tool name.

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 single-parameter lookup with an output schema and strong annotations, the description covers the purpose, the exact input format, the returned status categories, and the data source. It lacks only a brief note about how it differs from the sibling batch and verify tools, which is more of an usage-guidance gap than a completeness gap.

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%, with the business_number parameter already documented as a 10-digit Korean registration number with hyphens/spaces allowed. The tool description echoes this format and provides an example, but does not materially add semantics beyond the 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 checks the registration status of a Korean business using a 10-digit business registration number, and specifies the returned information (active/suspended/closed, tax type). It does not explicitly contrast itself with the sibling batch or verify tools, but the single-number input and status-return focus make its purpose identifiable.

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 explains that the tool checks a single Korean business by registration number, but it provides no guidance on when to choose this tool over check_korean_business_batch or verify_korean_business. There are no explicit when-to-use, when-not-to-use, or alternative-selection hints.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the behavioral detail that it checks a match against the registration number and returns 'match result plus current status.' However, it does not elaborate on edge cases, data-source behavior, or status semantics, and with an output schema present the return-value mention adds limited extra value.

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 three short sentences: purpose, use case, and return summary. Every sentence contributes distinct information, and the core verification behavior is front-loaded. There is 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?

For a single-record verification tool with full schema coverage, read-only/idempotent annotations, and an output schema, the description is largely complete. It states what is verified, when to use it, and what kind of result to expect. The only notable gap is lack of explicit differentiation from sibling tools, but that does not prevent correct invocation.

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 reinforces that representative_name and opening_date are used as match criteria, but it does not add meaning beyond the schema for business_number, address, or format details. The schema already documents formats and the optional address mapping.

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 uses a specific verb and resource: 'Verify that a Korean business registration number matches the provided representative name and opening date.' This clearly states the tool's core function. It does not explicitly distinguish itself from siblings like check_korean_business_status, but the 'match' framing and single-record verification make the purpose reasonably clear.

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 gives a clear usage context: 'Use for KYB / due-diligence before transacting with a Korean company.' This tells an agent when the tool is appropriate. It does not explicitly contrast with batch or status-only siblings, so exclusions and when-not-to-use guidance are missing, but the stated context is sufficient for most selection decisions.

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