FREE. Catalog of all asiaref reference-data series: supported countries, series ids, descriptions, freshness metadata and per-series URLs. Call this first to discover what data exists.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description notes 'FREE' and lists output contents. Sufficient for a read-only catalog tool, though could mention no destructive effects or authentication needs.
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; first gives essential information about output, second gives usage advice. No extraneous words, efficiently front-loaded.
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?
No output schema, but description enumerates the types of data returned (countries, IDs, descriptions, freshness, URLs). Combined with usage hint, fully informs agent of what to expect and how to use.
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?
No parameters in schema (0 params), so description doesn't need to add parameter info. Baseline of 4 applies as schema coverage is 100%.
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 explicitly states 'Catalog of all asiaref reference-data series' and lists what it provides, distinguishing it from sibling tools like get_series which likely target a specific series.
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?
Clearly instructs 'Call this first to discover what data exists,' giving a preferred usage order. Does not explicitly mention when not to use, but context with siblings implies alternatives.
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.
Each tool targets a distinct reference-data query: minimum wage check, income tax computation, VAT breakdown, working days, public holidays, series current/history/snapshot/catalog, and settlement date. There is no overlap; even get_series and get_series_history are explicitly separated as current vs. historical values.
Most tools follow a clear verb_noun pattern (check_, compute_, count_, get_, list_). The one outlier is settlement_date, which is a noun phrase rather than verb_noun, but the deviation is minor and does not cause confusion.
With 10 tools, the set is well-scoped for a reference-data server covering tax, labor, calendars, and settlement. Each tool serves a distinct purpose and none feel redundant or unnecessary.
The server provides strong coverage: discovery via list_series, current and historical series values, a bulk snapshot, and domain-specific computations for minimum wage, income tax, VAT, working days, holidays, and settlement dates. Minor gaps exist (e.g., no direct tool for social security contributions), but the core reference-data workflows are well supported.
