Korean Public Data
Server Details
Korean government open data - weather, population, law search via data.go.kr
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- SongT-50/korean-public-data-mcp
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 4.1/5 across 6 of 6 tools scored. Lowest: 3.2/5.
Each tool targets a distinct domain (business, air quality, economics, real estate, weather, and supported options), with no overlap in purpose.
All tool names follow a consistent verb_noun pattern (get_ for data retrieval, check_ for validation, list_ for enumeration), using snake_case throughout.
With 6 tools, the set is well-scoped for a public data server, covering multiple domains without being overwhelming or too sparse.
The server covers key public data areas, but lacks some potential additions like public holidays or business registration details beyond status. However, the provided tools are reasonably complete for the stated purpose.
Available Tools
6 toolscheck_business_registrationAInspect
사업자등록번호로 사업 상태를 조회합니다.
Args:
business_numbers: 사업자등록번호 리스트 (예: ["1234567890", "0987654321"]). 하이픈 없이 10자리 숫자. 최대 100개.
Returns:
각 사업자의 등록 상태 (계속사업자, 휴업자, 폐업자 등)
| Name | Required | Description | Default |
|---|---|---|---|
| business_numbers | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only query (조회) with no side effects, but does not explicitly disclose any behavioral traits such as required permissions, rate limits, or data source details. Since no annotations exist, the description carries the full burden, and it falls short of full transparency.
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 concise, with the purpose stated in the first sentence. The Args/Returns format is clear and well-structured, with no unnecessary 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?
Given the tool has one parameter and an output schema exists (though unseen), the description covers the essential aspects: purpose, parameter constraints, and return values. It does not explain error handling or edge cases, but remains largely adequate.
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 description adds significant meaning beyond the schema: it specifies the format (10 digits, no hyphen), maximum count (100), and example values. This compensates for the 0% schema coverage effectively.
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 queries business status using business registration numbers, using a specific verb '조회' and resource. It is distinct from sibling tools which cover unrelated domains like air quality, economics, etc.
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 provided on when to use this tool versus alternatives, nor any conditions or exclusions. The description lacks context for appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_air_qualityAInspect
실시간 대기질(미세먼지, 초미세먼지, 오존 등)을 조회합니다.
Args:
location: 지역명 (예: "서울", "강남", "부산", "제주"). 15개 주요 지역 지원.
Returns:
PM10, PM2.5, 오존, 이산화질소, 일산화탄소, 아황산가스 수치와 등급
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It discloses the return format (pollutants and grades) and mentions support for 15 major regions. It could be improved by noting data freshness or limitations.
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 concise with a clear purpose statement followed by Args and Returns sections. Every sentence adds value without 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?
Given the output schema exists, the description provides a good overview of return values. It lists all key pollutants and mentions regional support. However, it could specify whether the location is case-sensitive or if only Korean locations are supported.
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, but the description adds significant meaning by explaining the location parameter format (e.g., '서울', '강남') and stating it supports 15 major regions. This fully compensates for the lack of 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 clearly states it retrieves real-time air quality data including PM10, PM2.5, ozone, etc. It distinguishes itself from sibling tools like get_weather_forecast which is about weather, not air quality.
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 an example usage for the location parameter and lists the returned data. However, it does not explicitly state when to use this tool over alternatives or mention any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_economic_statsAInspect
한국은행 경제통계를 조회합니다.
Args:
indicator: 경제지표명. 지원 항목: 기준금리, 소비자물가지수, 실업률, GDP성장률, 수출액, 수입액, 원달러환율, 코스피
period: 조회기간. "latest"(최근 12개월), "2025"(특정연도), "202501-202602"(기간지정)
Returns:
해당 경제지표의 시계열 데이터
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | latest | |
| indicator | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
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 explains that the tool queries economic statistics and returns time series data. It implies a read-only operation without side effects, though it does not explicitly state non-destructive or authentication needs.
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 concise and well-structured with Args and Returns sections. Every sentence adds value, and the main purpose is front-loaded.
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 the tool's simplicity (2 params) and the presence of an output schema, the description adequately covers the tool's behavior, parameters, and return type ('시계열 데이터' - time series data). No gaps.
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 compensates fully. It lists all 8 supported values for 'indicator' and provides clear examples and default for 'period'. This adds crucial meaning beyond the schema's type/title.
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 verb '조회' (query) and the resource '한국은행 경제통계' (Bank of Korea economic statistics). It is distinct from sibling tools which cover business registration, air quality, real estate, weather, and options.
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 when to use this tool (to query economic indicators from the Bank of Korea) and lists specific supported indicators and period formats. It does not explicitly exclude alternatives, but siblings are unrelated, making this sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_real_estate_tradesAInspect
아파트 실거래가를 조회합니다.
Args:
district: 지역구 이름 (예: "강남구", "서초구", "성남시분당구"). 서울 25개구 + 주요 경기/광역시 지원.
year_month: 조회할 연월 (예: "202602"). YYYYMM 형식.
Returns:
해당 지역/기간의 아파트 실거래 내역 (단지명, 면적, 가격, 층, 거래일)
| Name | Required | Description | Default |
|---|---|---|---|
| district | Yes | ||
| year_month | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the data returned and the required inputs but does not disclose rate limits, authentication needs, or behavior on invalid inputs (e.g., empty results). The return format is partially described, but behavioral traits are minimal.
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 brief yet comprehensive, using a clear Args/Returns structure. Every sentence adds unique value without 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?
Given the simple two-parameter tool and the existence of an implied output schema, the description covers the essentials: region, time period, and expected return fields. It lacks details on edge cases or error handling, but for a straightforward retrieval tool, it 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?
Schema coverage is 0%, so the description must add meaning. It explains 'district' with examples and geographic scope (Seoul 25 districts + major cities) and 'year_month' with format (YYYYMM) and example. This is significantly more helpful than the schema alone, though some implicit constraints (e.g., exact spelling) are not detailed.
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 retrieves apartment actual transaction prices (실거래가), specifying resource and scope. It is well-differentiated from sibling tools which cover unrelated domains like business registration, air quality, etc.
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 for when to use the tool (querying apartment transaction prices by district and month) and gives format examples. It lacks explicit when-not-to-use statements, but the sibling tools are sufficiently distinct that confusion is unlikely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weather_forecastAInspect
도시별 단기 날씨 예보를 조회합니다.
Args:
city: 도시 이름 (예: "서울", "부산", "제주", "수원"). 25개 주요 도시 지원.
hours_ahead: 앞으로 몇 시간 예보를 볼지 (기본 24시간, 최대 72시간)
Returns:
시간대별 기온, 강수확률, 하늘상태 등 날씨 정보
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | ||
| hours_ahead | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations present, so description carries full burden. It explains return structure (temp, precipitation, sky condition) and parameter constraints (default/max hours). Adequate for a read-only weather 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?
Well-organized with Args/Returns sections, concise but informative. Minor redundancy, but overall efficient.
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 output schema exists (though not shown in detail), description covers parameters and return fields sufficiently. Completeness is high for a simple query tool.
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 description fully compensates by explaining city with examples, hours_ahead with default and max, and return details, providing complete parameter meaning.
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 tool retrieves short-term weather forecasts by city, with examples of city names and supported count, distinguishing it from unrelated 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?
Provides clear usage context: city-based forecast, with supported cities and hours range. Does not explicitly mention when not to use, but the purpose is specific enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_supported_optionsBInspect
이 MCP 서버에서 지원하는 도시, 지역, 경제지표 목록을 확인합니다.
Returns:
각 도구별 지원 옵션 목록
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
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 only states the return type but does not disclose side effects, safety implications, rate limits, or authorization needs. For a zero-parameter list tool, basic transparency is missing.
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 short sentences in Korean, front-loaded with the main purpose. No unnecessary words, efficient and clear.
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 no parameters and an output schema (context signal), the description is functional but lacks explicit mention that it helps validate inputs for other tools. It is adequate but not comprehensive.
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?
There are no parameters (0 params), so baseline score is 4. The description adds no parameter-specific information because none exist.
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 the tool checks the list of supported cities, regions, and economic indicators. It uses a specific verb and resource, clearly indicating what is returned. However, it does not explicitly differentiate from siblings, though the purpose is distinct enough.
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 on when to use this tool versus alternatives. It does not mention prerequisites, when not to use, or provide context for integrating with other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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