govdata-korea
Server Details
Cloudflare Workers MCP server: govdata-korea
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- lazymac2x/govdata-korea-api
- GitHub Stars
- 0
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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 3.5/5 across 6 of 6 tools scored.
Each tool targets a distinct aspect of Korean government data (business types, economic indicators, exchange rates, holidays, population, postal codes) with no overlapping purposes, making it easy for an agent to select the correct one.
All tools follow a consistent 'get_' prefix followed by the resource name in snake_case (e.g., get_business_types, get_economic_indicators), making the naming pattern predictable.
With 6 tools, the server is well-scoped for a focused government data provider, offering essential data categories without being overwhelming or too sparse.
The tool set covers major data categories like economic indicators, demographic, geo, and legal data, but missing some common government data such as education, health, or transport statistics, though not critical.
Available Tools
6 toolsget_business_typesBInspect
Look up Korean Standard Industrial Classification (KSIC) business type codes.
| Name | Required | Description | Default |
|---|---|---|---|
| code | No | KSIC code letter (A-U). Omit for all. | |
| search | No | Search term in English or Korean. |
Tool Definition Quality
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 only states 'look up,' implying a read operation, but provides no detail on output format, error handling, rate limits, or behavior when parameters are combined. The agent gains little beyond the name.
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 sentence of 12 words, which is concise and front-loaded with the action 'look up.' However, it may be overly terse given that parameter usage and output expectations are omitted. Still, 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?
The description is too minimal for a tool with no output schema and no annotations. It fails to explain what the returned data contains (e.g., code names, descriptions), how to use the search parameter effectively, or whether parameters can be combined. For a lookup tool, more context is needed.
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 100% with clear parameter descriptions. The baseline score is 3 because the schema already explains the parameters fully. The description adds no additional context about how parameters interact or expected values, so no improvement over baseline.
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 specifies a clear verb ('look up') and resource ('Korean Standard Industrial Classification (KSIC) business type codes'). It unambiguously identifies the tool's function and differentiates it from sibling tools like get_economic_indicators or get_holidays, which cover unrelated 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?
No guidance is provided on when to use this tool versus alternatives, nor any indication of prerequisites or limitations. While sibling tools are distinct, the description does not help an agent decide between the two optional parameters or when to use this over a general search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_economic_indicatorsBInspect
Get Korean economic indicators: GDP, CPI, unemployment, interest rate, trade, exchange rates, housing index.
| Name | Required | Description | Default |
|---|---|---|---|
| indicator | No | Specific indicator: gdp, cpi, unemployment, interest_rate, trade, exchange_rates, housing. Omit for all. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It does not mention that the tool is read-only, whether authentication is required, or how it handles invalid indicators. The description only lists what indicators are available, offering minimal 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 a single sentence that front-loads the action ('Get') and the scope ('Korean economic indicators'), making it immediately clear. No extraneous 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?
For a simple one-parameter tool with no output schema, the description is adequate but could be improved. It does not describe the return format (e.g., data structure, units, date ranges), which might be needed for correct invocation.
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 already describes the 'indicator' parameter well (listing allowed values and default behavior). The tool description lists the same indicators but adds no further meaning. Since schema coverage is 100%, baseline 3 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 what the tool does: 'Get Korean economic indicators' and lists specific indicators. This distinguishes it from sibling tools like get_exchange_rates or get_business_types, which have narrower scopes.
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 siblings. For example, if the agent needs only exchange rates, it should consider get_exchange_rates. The description lacks context for choosing among alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_exchange_ratesAInspect
Get KRW exchange rates against major currencies. Optionally filter by currency code (e.g. USD, EUR, JPY).
| Name | Required | Description | Default |
|---|---|---|---|
| currency | No | Currency code to filter (e.g. USD, EUR, JPY). Omit for all rates. |
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 for behavioral disclosure. It only states it 'gets' rates, implying read-only, but omits details like data freshness, rate limits, or response format. More context is needed.
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 efficiently convey purpose and optionality with no redundant words. Structure is front-loaded with the core action.
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 is simple (one optional param, no output schema). The description covers basic functionality but lacks details on return structure or behavior when omitted filter. Given no output schema, a brief note on output format would improve completeness.
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 100% description coverage for the single parameter. The description adds value by providing concrete examples (USD, EUR, JPY) that clarify the expected format beyond the schema's generic 'Currency code to filter'.
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 KRW exchange rates against major currencies, with a specific verb ('Get') and resource. It distinguishes from siblings like get_business_types and get_population by focusing on exchange rates.
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 indicates optional filtering by currency code, which guides usage. However, it does not explicitly state when to use this tool over siblings, though the distinct data domains make differentiation clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_holidaysAInspect
Get Korean public holidays for a given year (2024-2026).
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Year (2024, 2025, or 2026). Default 2026. |
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 for behavioral disclosure. It only states the basic function without revealing any side effects, rate limits, data source, or read-only nature. This is insufficient for 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 a single sentence that is extremely concise and front-loaded with the action and resource. Every word carries meaning with no wasted 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 tool with one parameter and no output schema, the description provides sufficient context to understand what the tool does and the valid years. However, it lacks information about the output format or any limitations, but given the simplicity, it is mostly 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 description coverage is 100%, and the description adds no significant meaning beyond what the schema already provides for the single parameter. The baseline of 3 is appropriate as the parameter is well-documented in 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 states the verb 'Get' and the resource 'Korean public holidays' with a specific scope (year range 2024-2026). It distinguishes effectively from sibling tools like get_business_types and get_economic_indicators.
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 usage for retrieving Korean holidays for a given year, but provides no guidance on when to use this tool versus alternatives, no when-not-to-use conditions, and no prerequisites. It is adequate but lacks explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_populationAInspect
Get South Korea population data by region. Based on 2024 KOSIS resident registration data.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | Region name in English or Korean (e.g. "Seoul", "서울"). Omit for all regions. |
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 states the tool fetches population data (implying read-only) and cites the data source, but does not disclose response format, error handling, or any behavioral traits beyond the basic operation.
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, front-loaded with the action and data source, with no extraneous information. Every word adds value.
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 tool with one optional parameter and no output schema, the description provides essential context (purpose, data source) but could optionally mention response structure or limitations, though not strictly necessary.
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 already describes the single parameter thoroughly (region name in English/Korean, optional). The tool description adds no extra semantic information beyond the schema, aligning with the baseline of 3 for 100% schema coverage.
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 South Korea population data by region, specifying the data source (2024 KOSIS resident registration data). This distinguishes it well from sibling tools covering business types, economic indicators, 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 does not provide explicit guidance on when to use this tool versus alternatives, though sibling tool names make the distinction obvious. No usage exclusions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_postal_codeBInspect
Look up Korean postal codes by district, city, area name, or postal code number.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query: district name, city, area, or postal code (e.g. "강남", "Gangnam", "06011"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states the basic lookup functionality, omitting details like authentication requirements, rate limits, output format, or any side effects. For a read-only lookup, this is minimal but not 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 a single sentence that efficiently conveys the action and scope. It is front-loaded with the purpose and immediately understandable, with no redundant words. Ideal for quick parsing.
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 simple schema (one required string) and no output schema. The description lacks information about the return value (e.g., postal code matches, address details) and any constraints like region-specific behavior. For a lookup tool, the agent would benefit from knowing what the response contains.
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 100%, with the schema already describing the 'query' parameter with examples. The description adds examples of input types (district, city, etc.) but does not provide new semantic meaning beyond what the schema offers. Baseline 3 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?
Description clearly states the action ('look up'), resource ('Korean postal codes'), and acceptable query types ('district, city, area name, or postal code number'). This uniquely identifies the tool's purpose and distinguishes it from siblings like get_business_types or get_population.
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 usage for looking up Korean postal codes but provides no explicit guidance on when to use it versus alternatives. The sibling tools are all different domains, reducing confusion, but the description does not mention exclusions or context-dependent choices.
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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