cigo-aircare-mcp

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

No annotations are provided, so the description carries the full behavioral burden. '실시간' (real-time) implies live/served data rather than cached, which is useful, but there is no mention of permissions, rate limits, failure modes, or whether the call has side effects despite the 'recommendation' wording.

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 front-loaded sentence with no filler. It is compact, though the packing of two concepts into one clause slightly hurts readability rather than aiding it.

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 no annotations, no output schema, and an undocumented required parameter, the description does not supply enough for an agent to call this correctly: the location format, the kind of result returned, and whether the tool is read-only are all unstated.

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 single 'location' parameter is undocumented in both schema and description. '지역별' loosely implies location is a region, but the expected format (city name, code, coordinates) is left entirely ambiguous, a real risk for a query keyed on geography.

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 names the domain of the check (per-region real-time weather, fine dust, and vapor risk) plus a filter-management recommendation, which is more than a tautology of the name. However, it fuses two seemingly different actions (diagnosis and recommendation) and uses a nominalized verb, so it is not immediately clear whether this tool reads conditions or also advises on filter maintenance.

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?

There is no statement of when to use this tool, when not to, or which alternative (e.g. search_carsafe) to prefer. The phrase '필터 관리 권장' hints at a use case but does not frame it as guidance for the agent's decision.

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 behavioral burden. It implies a read-only lookup via '조회', but says nothing about authentication requirements, whether it hits an external vendor API, rate limits, or how results are ordered or paginated.

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 front-loaded sentence with no filler. It is efficient, though it packs three concepts (inputs, filter specs, purchase links) into one dense clause.

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 two-parameter lookup with no output schema, the description covers the essentials of what goes in and roughly what comes out. With zero annotations and zero schema documentation, however, it leaves auth, error behavior, and result shape entirely unaddressed.

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 does map the two inputs conceptually ('차종' → model_name, '연식' → year), which is more than the bare schema offers, but it gives no format or range hints (e.g., whether year is a 4-digit integer, how model names are spelled).

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 names a specific verb (조회/lookup) and two concrete resources (맞춤 필터 규격 / 구매 링크) scoped to a car model and year. That is enough to distinguish it from siblings like check_environment or submit_b2b_inquiry, though the branded term '씨고 카세이프' is opaque to an outside agent.

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?

There is no explicit when-to-use guidance, no prerequisites, and no mention of alternatives among the sibling tools. The agent must infer that this is a product-lookup step from context alone.

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 behavioral burden, and it carries none of it. It does not disclose whether this writes/reserves data, requires authentication or consent, is reversible, or what happens on conflict with an existing reservation — all essential for a reservation-style mutation.

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?

It is a single short phrase with no filler, but that brevity is under-specification rather than conciseness. Nothing is front-loaded beyond a bare noun phrase, and the reader learns no actionable detail.

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 4-parameter, 3-required tool with no annotations and no output schema, this description is dramatically incomplete. The agent lacks purpose alignment with the name, usage context, behavioral disclosure, and all parameter meaning.

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% across four parameters, and the description compensates for none of it. It never explains what user_id, car_model, driving_time_or_freq mean, what format driving_time_or_freq accepts (minutes? frequency band?), or why driving_distance defaults to '모름' (unknown) and whether it may be omitted.

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 Korean phrase '운전자 주행 패턴 및 연락처 기반 교체주기 알림 예약' describes scheduling a replacement-cycle notification, but never mentions CRM, lifecycle, or anything matching the tool name/title 'set_crm_lifecycle'. An agent reading the name and the description side by side gets two different pictures of what the tool does, and no sibling differentiation is offered.

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?

There is no when-to-use guidance, no prerequisites, and no reference to the siblings (check_environment, search_carsafe, submit_b2b_inquiry). The agent is given no basis for choosing this tool over any 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?

With no annotations, the description carries the full behavioral burden. It does add useful context: original text is preserved, the customer's language code is passed through, and feasibility/price/certification/delivery are explicitly 'subject to review' (i.e., this is not an instant-quote tool). It says nothing about authorization, submission limits, or what happens after submission.

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 core English sentences are front-loaded and reasonably tight, but the final Korean sentence merely restates the opening in another language, adding length without new meaning. Field enumeration is dense but serviceable.

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 12-parameter submission tool with no annotations, no output schema, and 0% schema coverage, the description leaves too much unspecified – half the parameters, plus the post-submission behavior and any constraints on the 'subject to review' flow.

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% across 12 parameters, so the description must compensate. It names roughly six fields (company, contact email, country, subject, details, language code) but leaves manager_name, quantity, specifications, contact_phone, contact_info, and inquiry_category completely undocumented anywhere.

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?

States a specific verb and resource ('Accept worldwide B2B inquiries') and scopes it with the product domains it covers (HVAC/cleanroom filters, nanofiber materials, textiles, cosmetics). No sibling tool overlaps, so the agent can identify this immediately.

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 tells the agent what information to collect ('Ask for company, contact email, destination country, subject and details'), which is implicit usage guidance. However, it never states when this tool should or should not be used versus alternatives, nor any prerequisites (e.g., language handling rules as a precondition).

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