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Moltline RegClock

by com.moltlinestudio

Holiday Calendar

holiday_calendar
Read-onlyIdempotent

List the public holidays the engine uses for a country and year. FREE.

Typical input {"country": "IE", "year": 2026} returns {"holidays": [{"date": "2026-01-01", "name": "New Year's Day"}, ...]}. Use when checking why a business-day or bank-holiday adjustment landed where it did, or to see whether a country/subdivision is supported. Not a legal register of bank holidays: it is the holidays package's public-holiday calendar, which is what compute_deadlines uses. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "country is required"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYescalendar year.
subdivNooptional subdivision code (state, province, region).
countryYesISO 3166-1 alpha-2 code (DE, FR, IE, US, ...).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations, the description discloses important runtime behavior: invalid, missing, or malformed input never raises a protocol error and instead returns a structured error object with fix guidance. It also provides a concrete input/output example and confirms retry safety after correcting input. No contradiction with annotations.

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 mostly earns its length. The purpose, usage, example, error behavior, and safety note are all useful. Minor redundancy exists since read-only and idempotent are already in annotations, and 'FREE' adds no functional value for an AI agent.

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?

The description is complete for an agent to call the tool correctly: it states the input format, gives a typical example, explains the output shape, covers error behavior, and describes when to use it. Given the annotations and schema, nothing important for safe invocation is missing.

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 the baseline is 3. The description adds value beyond the schema by showing a typical input/output pair and clarifying that the tool can check whether a country/subdivision is supported, which gives practical meaning to the subdiv parameter.

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 opens with a specific verb and resource: 'List the public holidays the engine uses for a country and year.' It clearly distinguishes this tool from siblings by noting it is the calendar compute_deadlines uses, and explicitly says it is not a legal register of bank holidays.

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 gives explicit use cases: checking why a business-day/bank-holiday adjustment landed where it did, and checking whether a country/subdivision is supported. It also states a clear when-not: 'Not a legal register of bank holidays.' It does not name a direct sibling alternative, but the context is strong.

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

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct stage of the incident-reporting workflow: classify_event qualifies the incident, compute_deadlines and compute_deadlines_multi produce deadlines, deadline_status monitors them, explain_rule cites the underlying rule, and timeline_export and validate_report handle output and pre-submission checks. No two tools are plausible substitutes; the single vs. multi-regime split between the two deadline tools is explicitly described.

Naming Consistency3/5

Most action tools follow a verb_noun pattern (classify_event, compute_deadlines, explain_rule, list_regimes, validate_report), but three tools use noun_noun or reversed forms (deadline_status, holiday_calendar, timeline_export). The convention is readable but not uniform, making it a mixed pattern rather than a consistent one.

Tool Count5/5

Nine tools is a well-scoped set for a regulatory deadline engine. Each tool maps to a necessary capability—discovery, classification, computation, status, explanation, calendar data, export, and validation—without redundant or filler entries.

Completeness5/5

The surface covers the full lifecycle: discover regimes, classify an incident, compute single or multi-regime deadlines, assess their status, inspect the statutory rule, export to calendar/CSV, and validate a draft report. Holiday and regime metadata tools fill supporting gaps, leaving no obvious dead-end for the stated purpose.

Resources