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AIS Trail

Coverage and freshness

data_status
Read-onlyIdempotent

How many ships are live now (live_ships_private: how many of them are private vessels, counted only), how many are known from the last 7 days, the date range of the history archive per source with available_until (the latest time vessel_track can return for that source on plans without live data), and the service status: ok or degraded, with reason codes in reasons.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds real behavioral context beyond that: what live_ships_private counts (private vessels only, counted not listed), the 7-day lookback, what available_until means for vessel_track on live-less plans, and the degraded/reasons failure model.

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

Conciseness3/5

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

It is a single very long sentence with nested parentheticals, cramming four distinct concepts (live counts, 7-day counts, archive ranges, service status) into one clause chain. Information density is high, but the structure makes it harder to parse than a short bulleted breakdown would be.

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?

With no parameters, no output schema, and full annotation coverage of the safety profile, the description carries the return-value burden and does so credibly, enumerating the key response fields. Some field names (e.g. the 7-day known-vessel count) remain unnamed, so an agent cannot map every value to an exact key.

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 takes zero parameters, so per the rubric the baseline is 4. The description correctly implies no arguments are needed and instead spends its words explaining the response fields.

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

Purpose4/5

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

The description names a specific resource (coverage/freshness status) and enumerates the exact information returned: live ship counts, 7-day known vessels, per-source archive date range, available_until, and ok/degraded service status with reason codes. It is clearly distinguishable from the vessel-querying siblings, though it never explicitly frames itself as the metadata/health endpoint.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: an agent can infer this is a pre-flight check before querying vessel data, and available_until is tied to vessel_track's behavior on plans without live data. However, there is no explicit 'use this when...' or exclusion guidance, and no sibling tool is named as an alternative.

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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