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

With no annotations provided, the description carries the full burden. It discloses that the tool evaluates invalidation, abandonment, and term expiration beyond simple registration status, and that behavior changes if registered-ledger lookup is configured. It does not mention error handling or side effects, but the core behavioral traits are transparent.

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 compact, front-loaded with the main purpose, and each sentence adds a distinct piece of information: purpose, scope, usage timing, accepted formats, and conditional behavior. There is no filler or redundancy.

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 one-parameter read-only status check, the description explains the input format, the meaning of the result, and conditional output enrichment. It does not specify a formal output schema or error behavior, but at this complexity level the provided information is nearly complete.

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 already fully describes the single 'number' parameter, including format, hyphen optionality, and prefix rules. The description reinforces this with examples but does not add meaning beyond what the schema already provides.

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 ('판정합니다') and identifies the resource as a single patent/utility model number. It clearly states the output is an 'alive' status that accounts for more than just registration, which distinguishes it from generic search or citation 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?

It explicitly instructs agents to use this tool before making any patent-based claim, giving a clear trigger condition. However, it does not name sibling alternatives or state when not to use it, so some routing is left to inference.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure and excels at it. It reveals that all results include an alive/dead determination, that expired rights render as display:"dimmed", that aggregations like alive_in_inspected are based on the current determination count rather than total results, and that coverage_warning must be read. It also warns that the tool is not a final legal-status authority.

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, front-loaded with the core purpose, followed by dense behavioral caveats and usage guidance. Every sentence earns its place; there is 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?

Despite having no output schema and no annotations, the description sufficiently equips an agent to call the tool correctly: it explains the purpose, key output behaviors (alive judgment, dimmed display, aggregation semantics, coverage_warning), and the necessary follow-up with rights_alive for confirmation. What remains unspecified, such as exact result fields, is not required for 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 does not add parameter-level detail beyond the schema; its caveats concern output semantics rather than how to fill in query, limit, date_from, or applicant. This meets the baseline but does not exceed it.

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 opens with a specific verb and resource: '특허·실용신안을 키워드로 검색합니다' (searches patents and utility models by keyword). It also distinguishes itself from the rights_alive sibling by positioning search_ip as the broad search step and rights_alive as the confirmation step, so an agent can tell them apart.

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 names use cases ('선행기술 조사나 경쟁사 포트폴리오 파악에 쓰되') and gives an explicit when-not-to-rely-on-it rule: before using a specific case as a basis, call rights_alive again to confirm. This is direct guidance on when to use this tool vs the alternative.

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?

No annotations are provided, so the description carries the full burden. It discloses that the tool extracts all numbers, checks existence in KIPRIS, compares names, and checks whether rights are alive. It also mentions the KIPRIS call limit consideration in the max_citations parameter. It doesn't mention rate limits, failure behavior, or what happens when no citations are found, but the core behavioral traits are well covered.

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 two sentences, front-loaded with the core action and outcome, then the use case. Every sentence earns its place. The Korean text is dense but not bloated.

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 tool with 2 params, 100% schema coverage, and no output schema, the description covers the main behavioral aspects: what it verifies, how it works, and when to use it. It doesn't describe the return format, but with no output schema and a clear verification purpose, an agent can infer the result. The only gap is explicit failure/edge-case behavior (e.g., no citations found, invalid text).

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 schema already documents both parameters. The description adds context: '통째로' (whole text) reinforces the text parameter's purpose, and the KIPRIS call limit rationale for max_citations adds meaning beyond the schema's default/range. This is above the baseline 3 because the description explains why the limit exists.

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 purpose: it verifies whether patent numbers cited in text are real, checking existence in KIPRIS, name match, and whether the right is still alive. It also names the intended use case (LLM-generated technical documents, IR materials, patent analysis reports) and distinguishes it from siblings by focusing on whole-text verification.

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 says when to use this tool: when you have a full text with cited patent numbers and want to catch hallucinations. It doesn't explicitly say when not to use it or name alternatives, but the sibling context (rights_alive, search_ip) and the phrase '통째로 검증' imply this is for batch verification of a document rather than single-number lookup. A clear exclusion statement would push this to 5.

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