Korean Public Data
Server Details
Korean government open data - weather, population, law search via data.go.kr
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- SongT-50/korean-public-data-mcp
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 6 of 6 tools scored.
Each tool covers a distinct domain (business, air quality, economics, real estate, weather, and options listing), with no overlapping purposes. An agent can clearly distinguish between them.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., check_business_registration, get_air_quality). The verbs are appropriate and predictable.
Six tools is an appropriate size for a public data aggregation server. Each tool provides meaningful functionality without superfluous entries.
The tool set covers major Korean public data domains, but some common areas (e.g., public holidays, transportation) are missing. Within its stated scope, it is fairly complete.
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?
No annotations provided, so description bears full burden. It discloses max 100 items and output statuses, but lacks details on error handling, rate limits, or invalid inputs.
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?
Description is concise, front-loaded with purpose, followed by args and returns. No unnecessary sentences.
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?
Tool has output schema, so return details are covered. Description gives input format and output summary, sufficient for a simple lookup tool. Minor gap: no error behavior.
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%, but description adds format (10-digit number, no hyphen), max 100, and expected items, adding significant 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?
Description clearly states the tool queries business status by registration number, with specific inputs and outputs. It is distinct from siblings like get_air_quality and get_weather_forecast.
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?
Implicitly clear from context and sibling tools that this is for business registration checks. No explicit when-to-use or when-not-to-use, but differentiation is obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_air_qualityBInspect
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?
No annotations provided, and the description only mentions real-time aspect; fails to disclose rate limits, data freshness, permissions, or idempotency.
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?
Concise but well-structured with Args and Returns sections; no extraneous 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?
Given the tool is simple with one parameter and output schema exists, the description covers basics but lacks error handling or scope 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?
Despite 0% schema coverage, the description adds meaningful context by listing example locations (e.g., 서울, 강남) and stating 15 major regions are supported.
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 real-time air quality data including specific pollutants like fine dust and ozone, and distinguishes itself from sibling tools like weather forecast or 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?
No explicit guidance on when to use this tool versus alternatives such as get_weather_forecast or get_economic_stats; usage is implied but not stated.
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 only describes the basic operation without disclosing any behavioral traits such as data update frequency, rate limits, or error conditions. For a read-only query tool, this is lacking.
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, using a standard Args/Returns structure. It avoids unnecessary details but could be slightly more front-loaded. Overall, it is well-structured for a tool with two parameters.
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 2 parameters, no enums, and an output schema (not detailed), the description covers input adequately but does not mention any behavioral aspects like data freshness. For a simple query, this is minimally complete but could be enhanced.
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 adds significant meaning by enumerating valid indicator values and explaining period format options. This compensates for the bare schema, helping the agent understand correct 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 clearly states the tool queries Bank of Korea economic statistics, using a specific verb and resource. It distinguishes from siblings like get_weather_forecast or get_real_estate_trades by being focused on economic data.
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 detailed guidance on valid indicator values and period formats. While it doesn't explicitly state when not to use this tool, the sibling tools cover clearly different domains, making usage context clear.
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 transparency burden. It mentions the return fields (complex name, area, price, floor, transaction date) but lacks details on potential errors, pagination, authorization needs, or data recency. The behavioral details 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 concise, with a clear one-sentence purpose followed by structured Args and Returns sections. Every sentence adds value, no fluff.
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 low complexity (2 required params, no nested objects, output schema present), the description covers the main purpose, parameters, and return format adequately. Minor improvement: could clarify behavior for invalid input or data availability.
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 has 0% description coverage, but the description adds significant meaning: examples for district ('강남구', '서초구', '성남시분당구') and the YYYYMM format for year_month. It also notes supported regions. This compensates well for 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 the action ('조회합니다' - queries) and the resource ('아파트 실거래가' - apartment real transaction prices). It distinguishes from sibling tools like check_business_registration or get_weather_forecast by specifying real estate trade data.
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 parameter descriptions with format examples ('YYYYMM') and supported area details (Seoul 25 districts + Gyeonggi/metro cities). It does not explicitly state when to avoid this tool, but the context of the tool name and siblings makes usage clear.
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?
Without annotations, the description partially discloses behavior: it lists return fields (temperature, precipitation, sky condition). However, it omits details like error handling, data freshness, or side effects. A read-only nature is implied but not explicit.
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 very concise with a clean 'Args' and 'Returns' structure. Every sentence adds value; no redundant 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?
For a simple two-parameter tool with an output schema, the description is nearly complete. It explains inputs and outputs, though it lacks details on error handling or timezone. The presence of output schema reduces the need to detail return format, but description still covers it.
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 explains both parameters: city (with examples and supported count) and hours_ahead (default and max). This adds essential 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 the tool retrieves short-term weather forecasts by city with a specific verb ('조회') and resource ('날씨 예보'). It is unique among siblings (business registration, air quality, economic stats, real estate, options) and 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?
No guidance on when to use this tool versus alternatives or conditions to avoid. Does not mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_supported_optionsAInspect
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 exist, so description carries burden; it explains the output (list of options per tool) but lacks details on behavior like caching or error handling.
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 concise sentences front-load the purpose and output, with no wasted words.
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 parameters and an output schema present, the description adequately covers what the tool does, though it could be slightly more specific about output 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?
No parameters exist, so baseline score of 4 applies; description adds no parameter info but none is needed.
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 lists supported cities, regions, and economic indicators, distinguishing it from sibling data retrieval 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 as a setup/exploration tool before using data retrieval siblings, but no explicit when-to-use or when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!