HOTKER 핫커 | 한국 상품·휴대폰 성지 구매가 AI

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. The word '참고값' usefully signals that the result is a reference/estimate rather than a guaranteed bill, and '24개월' scopes the calculation. Still, it does not disclose assumptions, input relationships, or whether outputs are purely informational.

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 a single, front-loaded sentence with no filler. Every phrase adds meaningful information: the type of calculation, the inputs, and the 24-month reference-value nature of the result.

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 6 parameters, no schema descriptions, no annotations, and no output schema, the description is not complete enough for an agent to reliably invoke the tool correctly. It omits how optional and mutually relevant mobile-related parameters behave and gives no information about the return format or limitations.

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 must compensate, but it only names generic concepts: carrier, internet tier, and vague 'mobile line conditions'. It does not clarify the roles of iptv, mobileLines, mobilePlanFees, or mobileMonthlyTotal, nor how they should be supplied together.

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 action ('계산합니다' / calculates), a precise resource ('24개월 결합할인 참고값' / 24-month bundle discount reference value), and the main inputs (carrier, internet tier, mobile line conditions). This clearly distinguishes it from sibling tools that calculate penalties, total cost of ownership, or search verified products.

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 appropriate use case: calculating a 24-month bundle discount reference value from telecom and internet conditions. However, it does not explicitly state when to prefer this tool over alternatives such as calculate_mobile_tco or calculate_contract_penalty, nor does it mention exclusions.

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 of behavioral disclosure. It adds limited context by calling the result a 'basic reference value' rather than an official amount, but it does not disclose parameter dependencies, required inputs per penalty type, formula behavior, or output characteristics.

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 a single concise sentence with no filler and is front-loaded with the operation and scope. However, it is too sparse to cover the semantic load of a 7-parameter calculator, so brevity borders on under-specification.

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 7-parameter calculator with no annotations and no output schema, the description is incomplete. It omits conditional parameter usage, the meaning of elapsed time units, how subsidy/discount values factor in, and what the returned 'reference value' looks like.

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 must compensate. It maps penaltyType values to concepts (선택약정 vs 공시지원금) and carrier values to SKT/KT/LG U+, but it does not clarify elapsedDays versus elapsedMonths, contractMonths, receivedSubsidy, accumulatedDiscount, or which parameters are relevant for each penalty type.

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?

Description clearly states the specific action ('계산합니다') and the resource (SKT·KT·LG U+ 선택약정 할인반환금 또는 공시지원금 반환액). It makes the tool's domain and function obvious, though it does not explicitly distinguish it from siblings like calculate_combo_discount or calculate_mobile_tco.

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?

No guidance is given about when to use this tool versus alternatives. It only says it computes a '기본 참고값' (basic reference value), which suggests scope but does not state prerequisites, exclusions, or when another sibling would be the better choice.

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, the description carries the full burden of behavioral disclosure. It usefully states that it will explicitly mark missing/estimated conditions and that it will not fabricate real-time prices. It does not mention other behavioral traits such as data mutation, authentication requirements, or error behavior for invalid input combinations, but for a calculation tool the disclosed constraints are meaningful.

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?

Two sentences with no redundancy. The core action, input scope, and key limitation are front-loaded and every clause adds information. It is appropriately sized for the complexity it addresses.

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?

The tool has a large nested deal object, four top-level parameters, and no output schema or annotations, yet the description is only two sentences. It does not specify the return value format, how horizonMonths is applied, which conditions are considered missing/estimated, or how the total cost is composed (fees, installments, etc.). For this complexity, the description is incomplete.

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 must compensate. It names a few broad input categories (device price, plan hold period, add-ons, confirmed discounts) that map to some fields, but the actual schema is large and nested with fields like subsidy_type, mandatory_costs, installment_months, and selection_discount_policy. The description does not explain these or their roles, so it only partially compensates for the missing schema descriptions.

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 ('계산합니다' – calculates) and resource ('12~36개월 총비용' – total cost over 12–36 months), and names the key inputs: device price, plan hold period, add-ons, and confirmed discounts. The clause 'does not create real-time sale prices' helps distinguish it from search/deal sibling tools. This is clear and specific, not a tautology.

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 clear context that this tool computes total cost from user-provided deal data and explicitly says it does not produce real-time sale prices, which offers some negative guidance. However, it never names sibling alternatives (e.g., calculate_contract_penalty) or states conditions for choosing between them, so usage guidance remains implied rather than explicit.

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 present, so the description carries the burden. It clearly signals a read-only retrieval action and discloses the output categories: engine version, official carrier sources, and limitations. It doesn't cover auth, rate limits, or errors, but for a simple getter it provides meaningful behavioral context.

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 one dense, front-loaded sentence with no filler. Every phrase contributes information about what the tool returns and for which calculation contexts.

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?

The description adequately summarizes output contents for a low-complexity getter, but there is no output schema and no annotation support. It lacks parameter usage guidance and explicit differentiation from calculation siblings, leaving an agent to infer some decisions.

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 never mentions carrier or calculationType directly. It indirectly maps to calculationType values by naming TCO, contract refund, and combo discount, but it does not explain how to select values or the meaning of 'all', nor does it clarify carrier usage.

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 ('returns') and a concrete resource: engine versions, official carrier sources, and limitations for the calculation formulas. It also enumerates the covered calculation types (TCO, contract refund, combo discount), which distinguishes it from the calculate_* sibling 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?

Usage is implied: when an agent needs evidence, sources, or version info rather than performing a calculation, this is the tool. However, the description never explicitly says when to use this tool versus the calculate_* siblings, and there is no exclusions or alternative guidance.

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 present, the description carries the full behavioral burden and does disclose the core trait: the tool aggressively filters results, excluding non-official, expired, estimated, or incomplete conditions, so an agent learns it will never return partial or unverified data. It does not, however, disclose return format, ordering, pagination, or failure behavior, leaving part of the burden unmet given zero annotation coverage.

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?

A single dense Korean sentence with no filler; every clause adds a distinct criterion (verified status, remaining validity, no estimates/omissions, 24-month TCO). It could arguably be split for readability, but there is nothing to remove.

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?

The description fully defines the tool's result domain, which is the core information an agent needs to select it. It falls short on call-shaping details—parameter meanings, what returned objects look like (no output schema exists), and how limit interacts with the strict filter—leaving moderate ambiguity before invoking.

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 was required to compensate, but it never touches the three parameters: what query should contain, how limit caps results, or the meaning of carrier values. The schema's own constraints (limit 1–10 with default 5; carrier enum of SKT/KT/LGU+/MVNO) are partly self-documenting, but the free-text query has no semantic guidance anywhere in the 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 pairs a specific verb (검색합니다/searches) with a precisely scoped resource: 24-month TCO mobile phone conditions that are official/carrier-verified, have remaining validity, and contain no estimates or omissions. This filter criteria set clearly separates it from the sibling search_verified_products, which targets products rather than TCO conditions. There is no tautology or ambiguity.

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 '만' (only) framing signals that this tool is deliberately narrower than a general search, implying it should be chosen when verified, complete, in-effect 24-month TCO deals are required. However, it never names a sibling or states when to prefer search_verified_products over this tool, so routing is left to inference rather than explicit guidance.

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 behavioral disclosure burden. It meaningfully discloses that only SEO-INDEX-passed products are returned and that NOINDEX, REVIEW, and collecting products are filtered out. It does not mention result ordering, pagination, or auth/rate limits, but the core filtering behavior is 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 two sentences with no filler. The core scope and exclusions are front-loaded, and every phrase adds useful information.

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?

The description is sufficient for understanding the tool's selection scope, but it is incomplete for confident invocation because parameter semantics are absent and there is no output schema to clarify the return shape. Given no annotations and no output schema, the description should provide a bit more operational detail.

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 and the tool description provides no information about the query, category, or limit parameters. Parameter names are somewhat self-explanatory, but the description does not compensate for the missing schema descriptions, leaving semantics like accepted category values and query behavior undocumented.

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 verb ('search'), the resource ('HOTKER production catalog'), and the specific inclusion criteria ('SEO INDEX passed, latest products'). It also names exclusions ('NOINDEX, REVIEW, collecting'), making its purpose precise and distinguishable from sibling tools like search_verified_mobile_deals.

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 states what will and will not be returned, giving a clear scope boundary. However, it does not explicitly mention when to prefer this over alternatives or provide any exclusionary guidance for sibling tools, so the usage context is implied rather than explicit.

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