Skip to main content
Glama

Moltline RegClock

List Regimes

list_regimes
Read-onlyIdempotent

List the reporting regimes this server can compute, with citations. FREE.

Typical input {} returns {"regimes": [{"id": "eu_nis2", "name": ..., "instrument": ..., "applies_from": ..., "event_types": [...]}, ...], "verified_on": "2026-09-06"}. Use when choosing the regime id and event_type for compute_deadlines or classify_event. Not for legal advice: it reports what the instruments say and when the entry was last checked. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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?

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the bar is lower, but the description adds substantial context: it reports what instruments say, includes a verified_on timestamp, never raises protocol errors but returns an error object, and is safe to retry. This goes well beyond the structured annotations and gives the agent accurate expectations.

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 front-loaded with the core purpose and uses the example and usage note effectively. It is slightly longer than necessary because it repeats the read-only/idempotent claims already present in annotations and includes the arguably unnecessary 'FREE' emphasis, but every other sentence earns its place.

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?

For a zero-parameter listing tool with rich annotations, the description is complete: it gives a concrete response example, explains when to use it, discloses limitations, specifies error behavior, and confirms retry safety. An agent has everything needed to call it correctly.

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?

The tool has zero parameters and the schema already documents an empty object. The description reinforces this with 'Typical input {}' and explains the shape of the response, which is helpful even though no parameter-level semantics are needed. This matches the 0-parameter baseline.

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 reporting regimes this server can compute, with citations.' This clearly identifies what the tool does and distinguishes it from the sibling compute/classify operations by framing it as the discovery step for regime ids and event_types.

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?

It explicitly says when to use the tool: 'Use when choosing the regime id and event_type for compute_deadlines or classify_event.' It also adds a clear non-purpose boundary ('Not for legal advice'). It does not explicitly name an alternative tool to use instead, but the usage context is strong enough to route an agent correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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