FREE. Catalog of all usaref 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?
With no annotations, the description carries the burden of disclosure. It lists all output elements (countries, IDs, descriptions, freshness metadata, URLs) and notes 'FREE', indicating no cost. No contradictions or omissions for a read-only catalog.
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, no wasted words. Front-loaded with 'FREE' and 'Catalog of all usaref reference-data series', and each phrase adds essential information.
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?
Despite lacking an output schema, the description fully details what the tool returns. For a zero-parameter catalog tool with good annotation coverage, no additional context is needed.
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 has no parameters, and description coverage is 100%. The description adds value by clarifying the tool's role as an initial discovery step, which is appropriate for a parameterless tool.
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: catalog all usaref reference-data series, including supported countries, IDs, descriptions, freshness metadata, and URLs. It also advises to call this first, distinguishing it from sibling tools like get_series which fetches 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?
Explicitly instructs to call this first to discover existing data, providing clear usage context. While it doesn't list when not to use, the simplicity of a no-parameter catalog tool makes this guidance sufficient.
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 have clear, distinct purposes: minimum wage checking, income tax, VAT, working days, holidays, settlement dates, and generic series access all serve different functions. However, compute_vat and check_minimum_wage could be confused with simple get_series lookups for the same series (vat, minimum-wage), though the descriptions clarify that the compute_* tools provide breakdowns rather than just current values.
The majority of tools follow a consistent verb_noun snake_case pattern (check_minimum_wage, compute_income_tax, compute_vat, count_working_days, get_public_holidays, get_series, get_series_history, get_snapshot, list_series). Settlement_date breaks the pattern as a noun-only name instead of something like get_settlement_date, creating a minor deviation.
With 10 tools, the server is well-scoped for its reference-data purpose. It covers a broad but justified range of statutory and financial reference data without being excessive, and each tool serves a distinct function within that domain.
The tool set provides comprehensive coverage of typical reference data: minimum wage, income tax, VAT, public holidays, working days, settlement dates, and generic series with current values, history, and snapshots. The inclusion of list_series for discovery and the generic get_series/get_series_history endpoints cover the full lifecycle of accessing reference data. No obvious gaps for the stated purpose.
