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"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, placing full burden on the description. While 'look up' implies a read-only operation, the description does not explicitly state safety, return behavior, or limitations. It provides only the core purpose without additional behavioral context.
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, dense sentence that front-loads the verb and resource, with no unnecessary filler. Every word contributes to understanding the tool's core function.
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 lookup tool, the description provides enough context to understand purpose and input format. While there is no output schema, the implicit return of postal codes is clear from the description, though explicit mention of return values would improve 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?
The input schema fully documents the single parameter with a clear description and examples, achieving 100% schema coverage. The tool description's mention of 'district, city, area name, or postal code number' adds minimal value beyond what the schema already states, so the 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's purpose with a specific verb ('look up') and resource ('Korean postal codes'), and enumerates the types of query inputs. This distinguishes it from the sibling 'get_' tools which retrieve different data categories.
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 implicitly signals when to use this tool (when a Korean postal code is needed), and the sibling tools are clearly distinct in function. However, it does not explicitly mention when not to use it or present alternatives, so it stops short of a 5.
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.