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reference-data

by dev.euroref

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

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: it is a paid tool ($0.05), uses calendar intersection with per-day trail, cites sources, and refuses free guesses. It also explains calendar types and special rules, adding transparency beyond what would be expected.

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 detailed and front-loaded with cost and purpose. While it contains many specifics, each sentence adds value for a complex tool. It could be slightly more concise but is well-structured for an AI agent.

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

Completeness4/5

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

Given the complexity (6 parameters, no output schema, no annotations), the description is thorough. It explains refusal logic, calendar intersection, and trail output. However, without an output schema, the return format could be more explicitly described, but the mention of 'per-day trail' provides adequate context.

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%, meaning all parameters have descriptions. The description adds meaning by explaining how parameters like business_days vs market+instrument_class work together, and clarifies that government bonds never inherit equity cycles, which goes beyond the schema.

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 based on business days and calendar intersection, with specific mention of a per-day trail. It distinguishes itself from sibling tools like count_working_days and get_public_holidays by focusing on settlement date calculation.

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

Usage Guidelines4/5

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

The description provides explicit guidance on when to use the tool (for settlement date computation) and when not (refuses guesses, no cited cycle, etc.). It implies alternatives through refusal conditions but does not name sibling tools directly. The usage context is clear enough.

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 operation: minimum wage, income tax, VAT, working days, holidays, series data, and settlement dates. No overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., check_minimum_wage, compute_income_tax, get_public_holidays, list_series). Only 'settlement_date' is a noun phrase but still clear.

Tool Count5/5

10 tools cover a well-scoped domain of economic reference data without bloat. The count feels appropriate for the server's purpose.

Completeness4/5

Covers core areas: wage, tax, VAT, holidays, series, settlement dates. Minor gaps exist (e.g., no social security or currency data), but the scope is clearly defined and noted.

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