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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does not mention what the response looks like, whether all indicators are returned together, if the data is historical or current, or any special behavior like formatting. It only lists the indicators, leaving significant behavioral unknowns for the agent.
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 purpose and scope. It is front-loaded with the verb 'Get' and the resource, followed by a specific enumeration. No redundant words or phrases.
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?
This is a simple tool with one optional parameter and no output schema. The description provides enough to understand the general purpose, but it lacks context on return structure, update frequency, or how it relates to the sibling get_exchange_rates. For a tool of this simplicity, the gap is moderate.
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 covers the single optional parameter fully, including a list of valid values and the default behavior when omitted. The description's mention of indicator names adds little beyond the schema. Since schema coverage is 100% and the description doesn't add extra parameter details, 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's function: 'Get Korean economic indicators' and provides a specific list of indicators. This distinguishes it from most sibling tools like get_population and get_holidays, though it overlaps with get_exchange_rates which is a sibling. Still, the verb and resource are clear.
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. For instance, since get_exchange_rates exists as a sibling, it would be helpful to note whether to use this tool or that one for exchange rate data. The description only states what it does, not when to choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.