Korean Public Data

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

With no annotations, the description carries the full burden. It communicates this is a read-style lookup ('조회합니다' — queries), defines input limits (maximum 100), and enumerates expected statuses (계속사업자, 휴업자, 폐업자 등). It does not mention error handling or network dependencies, but for a simple query tool the behavioral disclosure is adequate and non-misleading.

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 core purpose, and then organized clearly into Args and Returns sections. Every line adds value: input format, example, limit, and expected output statuses. No redundant or vague wording is present.

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-parameter lookup tool, the description covers everything needed to invoke it correctly: the meaning of the parameter, its format constraints, the batch limit, and the kind of result returned. Since an output schema reportedly exists, the description appropriately does not need to detail the full return structure.

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 schema provides no description coverage for business_numbers, but the description fully compensates: it identifies the parameter, gives a concrete example, requires 10-digit numeric strings without hyphens, and sets a maximum of 100 items. This is exactly the kind of semantic detail an agent needs beyond the raw array-of-strings 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 states a specific action and resource: '사업자등록번호로 사업 상태를 조회합니다' (queries business status by business registration number). This clearly distinguishes it from sibling tools like get_air_quality or get_weather_forecast, which cover entirely different domains.

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 makes the use case obvious: when a caller has a Korean business registration number and wants to know the business status. It also gives concrete input constraints (10 digits, no hyphens, max 100 numbers), giving clear context. It does not explicitly name alternatives, but the sibling tools are unrelated, so no exclusion is necessary.

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. It discloses real-time behavior, location limitations, and the returned pollutant types/grades. It does not mention units, possible errors for unsupported locations, or data update cadence, but it covers the core behavioral contract.

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 uses clean Args/Returns sections. Every sentence earns its place with no redundant filler.

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-parameter read-only lookup, the description provides enough to call the tool successfully for typical inputs. It could be improved by listing the 15 supported regions or pointing to list_supported_options, but the examples and return summary make it largely sufficient.

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 schema defines location only as a string with no documentation, so the description provides meaningful additional guidance: location type, examples like 서울/강남/부산/제주, and a 15-region support limit. It stops short of listing the exact supported regions, which would be more complete.

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 fetches real-time air quality data and enumerates specific pollutants (미세먼지, 초미세먼지, 오존 등). It names a distinct resource and is easily distinguished from siblings like get_weather_forecast or get_economic_stats.

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 practical location examples and notes that 15 major regions are supported, which implies usage context. However, it does not explicitly say when to prefer this tool over alternatives or how to confirm whether a particular location is supported.

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 behavioral disclosure burden. It conveys a read-only inquiry via '조회' and states the return shape as time-series data, but it does not discuss data latency, possible gaps in coverage, or error/edge-case behavior. Useful but not fully 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 and well-organized: a one-line purpose followed by Args and Returns sections. Every part adds relevant information, with 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 low-complexity, two-parameter read-only tool with an output schema, the description covers purpose, all parameter semantics, and the return type. Minor gaps remain around data units/frequency and potential error behavior, but these are not critical given the output schema and simple API.

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%, but the description fully compensates: indicator lists all supported values, and period gives explicit format examples including 'latest', '2025', and '202501-202602'. This adds substantial meaning beyond the bare schema properties.

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 and resource: querying Bank of Korea economic statistics. It further enumerates supported indicators, and those indicators clearly distinguish this tool from its unrelated siblings (weather, air quality, real estate, business registration).

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 by the description: use this tool when Bank of Korea economic indicators are needed and the supported indicator list signals applicability. However, there is no explicit guidance about when to prefer this tool over alternatives, and sibling list_supported_options is not referenced.

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. It clearly explains the return payload (complex name, area, price, floor, transaction date) and the supported region scope (Seoul 25 districts + major Gyeonggi/metro). While it doesn't mention rate limits or error handling, for a read-only query tool the disclosure is adequate.

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 clean docstring with Args and Returns sections. The purpose is the first sentence, and every sentence serves a purpose — no redundancy. It's compact and well-structured.

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 query tool with an output schema, the description covers both parameters, the supported scope, and the return fields. Siblings are unrelated, so no routing confusion. Nothing essential 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 0%, so the description must fully compensate. It does: 'district' gets concrete examples (강남구, 서초구) and the supported region list, and 'year_month' gets a format example (202602) and the YYYYMM rule. This adds substantial meaning beyond the bare 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 it queries apartment actual transaction prices (아파트 실거래가). It uses a specific verb (조회) and resource, and the example districts distinguish it from all siblings, which cover unrelated domains like business registration, air quality, and weather.

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 provides clear context on what inputs are needed (district and year_month) with format examples, and it's obvious from the sibling names that this is the only real-estate tool. It does not explicitly list when not to use it or alternatives, but that's unnecessary given the distinct domain.

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 behavioral burden. It discloses that this is a read-only query ('조회합니다'), supports 25 major cities, limits forecast range to 72 hours, and describes the return contents. It does not cover edge-case behavior such as unsupported city handling or units, but it is reasonably transparent for a simple forecast tool.

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 and well-organized. The purpose is stated up front, and the Args/Returns format makes parameter semantics easy to scan. No unnecessary elaboration or repetition.

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 an output schema exists, the description does not need to fully restate return details. It covers required input semantics, supported cities, and limits. A slight gap is that the full list of 25 supported cities is not provided, but the examples and sibling 'list_supported_options' make this manageable.

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 coverage is 0%, so the description must explain the parameters itself. It does so well: 'city' gets example values and supported-city scope, and 'hours_ahead' gets a default of 24 and an explicit maximum of 72. This adds real meaning 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 identifies the resource (short-term weather forecast), the action (lookup/query), and the scope (city-based). It is specific enough to distinguish from siblings like get_air_quality and get_real_estate_trades.

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 makes its intended use clear: retrieving short-term weather forecasts for supported Korean cities. It does not explicitly mention alternatives or when not to use it, but the sibling tools are distinct enough that the context is clear.

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 only says it returns a list of supported options; it does not mention that it is a read-only operation with no side effects, nor does it disclose any authentication requirements or potential delays. The minimal description leaves important behavioral traits unaddressed.

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 that front-load the purpose with no superfluous words. Every sentence contributes to understanding what the tool does and what it returns.

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 metadata tool with no parameters and an output schema present, the description is minimally adequate. However, it lacks explicit context that this tool provides valid options for other tools (e.g., for use as parameter values), which would make it more actionable. It does not describe the return format or structure beyond a generic list.

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 tool has zero parameters, so the baseline is 4. The description adds nothing about parameters (there are none), and the schema is empty with 100% coverage. The description does not need to explain parameters, so the baseline is appropriate.

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: to check the list of cities, regions, and economic indicators supported by the MCP server. It identifies a specific resource (supported options) and a clear verb (check), and it distinguishes itself from data-fetching siblings by being a metadata/discovery 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?

Usage is implied rather than explicit. The description does not state when to use this tool versus the data tools, nor does it mention that it should be consulted to obtain valid parameter values for sibling tools. There is no direct guidance, but the context of sibling names suggests it's a discovery helper.

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