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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It mentions the 2024 KOSIS data source, which adds context about data recency. However, it does not describe the return format, whether population is broken down by age/gender, or any limitations. Basic read-only behavior is implied but not explicitly stated.
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, clear sentence that immediately conveys the tool's purpose and data source. It is concise and front-loaded with relevant information, 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?
The tool is simple (one optional parameter) and the description provides essential context (data source, region specificity). While there is no output schema, the description and tool name sufficiently convey the general output. The absence of more detail on return structure is a minor gap but not critical for a straightforward data retrieval 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?
The schema fully documents the 'region' parameter with examples and the default behavior ('Omit for all regions'). The description adds no additional parameter meaning beyond what the schema already provides, so a baseline of 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 the tool retrieves South Korea population data by region, using a specific verb ('Get') and resource ('population data'). It distinguishes itself from sibling tools like get_economic_indicators and get_business_types, which target other data types.
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 population-related queries and clearly identifies the geographic scope. However, it does not explicitly state when to use this tool over alternatives or mention exclusion criteria, though the purpose is so specific that guidance is effectively conveyed.
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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Most tools are clearly distinct (business types, holidays, population, postal codes), but get_economic_indicators includes exchange rates in its list, overlapping with the dedicated get_exchange_rates tool. The descriptions clarify the different levels of detail, so the ambiguity is minor.
Five tools use the 'get_' prefix while one uses 'lookup_' (lookup_postal_code). The verb_noun pattern is dominant, but the mixed prefix is a slight inconsistency. The naming is otherwise clean and predictable.
With 6 tools covering key Korean government data areas (business codes, economic indicators, exchange rates, holidays, population, postal codes), the count is well-scoped. Each tool represents a distinct data domain and none are redundant or superfluous.
The set covers major statistical and reference data for South Korea, but the overlap between economic indicators and exchange rates suggests the domain is not fully nailed down. It lacks other common public datasets (e.g., taxation, education), but the core categories are present and usable.