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. |
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 does not state whether the tool reads live data, requires authentication, has rate limits, or how it behaves with an invalid currency code. It only states the basic action, leaving significant behavioral context undisclosed for an unannotated 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?
The description is two short sentences, front-loaded with the core purpose and then the optional parameter. Every word earns its place; there is no redundancy or filler.
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-optional-parameter lookup tool with no output schema, the description covers the essential purpose and filtering behavior. It lacks deeper context like return format or frequency, but the simplicity limits the need. Given no annotations, a score of 4 reflects that it is nearly complete for its scope.
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 already provides 100% coverage for the single parameter 'currency', including the example values and omission behavior. The description adds no additional semantic detail beyond what the schema states, so 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 'Get KRW exchange rates against major currencies', using a specific verb and resource that distinguishes it from sibling tools like get_economic_indicators or get_holidays. The optional filter for currency code adds precision without ambiguity.
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 when to use this tool: whenever KRW exchange rates are needed, with optional currency filtering. Sibling tools cover other domains (business types, economic indicators, holidays, population, postal codes), so the context is clear, though no explicit exclusions or alternative tool references are given.
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.