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reference-data

by dev.ausref

settlement_date

PAID ($0.05). Settlement/value date: the date n business days after a trade date, computed on the INTERSECTION of the calendars you name, with a per-day trail showing every skipped day and why, on which calendar. Supply business_days for a cycle you are trading under, or market + instrument_class to have a settlement cycle applied that we cite to its primary source (e.g. US equities T+1 under 17 CFR 240.15c6-1(a); UK gilts T+1 by DMO convention, which is NOT the T+2 equity cycle; Japan equities T+2 but JGBs T+1). Calendars are NATIONAL statutory holiday calendars with researched statutory weekend rules (Israel Saturday-only, Gulf Friday+Saturday, India Sundays plus the 2nd and 4th Saturday), NOT CSD or exchange calendars — each response states the basis and any known divergence. Use 'eu.t2' for the euro cash leg. We refuse FREE rather than guess: beyond published calendar coverage, no cited cycle for the instrument (government bonds never inherit an equity cycle), a cycle not in force on the trade date, or FX spot value dates. Free companion: GET /settlement-conventions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNoISO country of the market whose cited cycle should apply (with instrument_class)
api_keyNoAPI key (bypasses x402; metered for invoicing)
calendarsYesCalendar ids to intersect: two-letter ISO country codes, plus 'eu.t2' for the euro cash leg
trade_dateYesTrade/reference date, YYYY-MM-DD. Day 0: never counted, never rolled.
business_daysNoThe offset you are trading under (T+n)
instrument_classNoInstrument class — a government bond never inherits an equity cycle

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description fully discloses behavioral traits: it charges $0.05, shows a per-day trail, refuses to guess, uses national statutory holiday calendars (not CSD/exchange), and cites primary sources for cycles. It also notes known divergences and refusal conditions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded with the core purpose. It efficiently packs many details (pricing, cycle examples, calendar definitions, refusal conditions) into a single paragraph, making it comprehensive for a complex tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, no output schema, no annotations), the description is remarkably complete. It covers all essential aspects: how to invoke, what to expect, edge cases, pricing, and data sources, leaving no significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds significant meaning beyond the schema by explaining how business_days and market+instrument_class interact, calendar codes like 'eu.t2', and the meaning of trade_date (day 0 never counted/rolled). This justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool computes settlement/value dates as n business days after a trade date using the intersection of specified calendars. It distinguishes itself from sibling tools (none of which handle settlement date computations), and provides specific examples of cycles and jurisdictions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use the tool (e.g., 'Supply business_days for a cycle you are trading under, or market + instrument_class') and when not (e.g., 'We refuse FREE rather than guess: beyond published calendar coverage, no cited cycle...'). It also explains how to specify calendars and the 'eu.t2' for euro cash leg.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct domain: wage compliance, income tax, VAT, working days, holidays, series data (current, history, snapshot, catalog), and settlement dates. Even the financial computations (check_minimum_wage, compute_income_tax, compute_vat) have clearly different inputs and purposes, so no two tools are confusable.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case pattern (check_, compute_, count_, get_, list_). The only exception is `settlement_date`, which is a noun phrase rather than verb-first, but it remains consistent in style and readability. This minor deviation prevents a perfect score.

Tool Count5/5

With 10 tools, the server is well-scoped for a reference-data provider. It balances specific computations (minimum wage, tax, VAT) with general data access (series, holidays, settlements), and each tool serves a clear purpose without overlap or unnecessary bulk.

Completeness5/5

The tool surface comprehensively covers reference data for financial and calendar domains: statutory rates and compliance, holiday and working-day calendars, settlement conventions, and a generic series system with current, historical, snapshot, and catalog access. No obvious gaps impede an agent from accomplishing typical reference-data tasks, and the free list_series enables discovery.

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