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incident_history

Reconstruct how incidents built up over a recent window with per-incident threat-level timelines and scores, revealing event sequence so you can drill into active cases.

Instructions

Timeline of how each incident built up over the recent window — per incident, a compact series of [time, threat_level, score] points. Reveals sequence (e.g. jamming first, then dark vessels, then a reported event). Global uses the background watch history; an AOI uses your prior watch calls.

The full window can hold many incidents; this returns the limit most active ones (default 25, alias max_incidents) with each series downsampled to its threat-level transitions, and reports incident_count / returned / truncated so you know if more exist. Raise limit or narrow with lat/lon/hours to drill in.

Input Schema

NameRequiredDescriptionDefault
latNo
lonNo
hoursNo
limitNo
detailNoshort
radius_nmNo
max_incidentsNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.0

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses downsampling to threat-level transitions, the default of 25 most-active incidents, that the result is truncated, and that incident_count/returned/truncated are reported. It omits that this is a read-only lookup and says nothing about permissions or cost, leaving a small gap.

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?

Two well-organized paragraphs: the return shape and sequence insight come first, followed by truncation/limit behavior and tuning advice. Slightly wordy with markup, but every sentence carries information.

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

Completeness3/5

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

An output schema exists, so return values need little extra explanation, and the truncation semantics are covered. But for a 7-parameter tool with 0% schema coverage, the undefined detail and radius_nm parameters and the absent when-to-use-vs-siblings guidance leave the definition incomplete.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate for 7 undocumented parameters. It explains limit (default 25, alias max_incidents) and the lat/lon/hours narrowing, but detail and radius_nm are never mentioned anywhere, leaving meaningful gaps.

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?

States a specific resource (incident timelines) and the exact return shape: a per-incident series of [time, threat_level, score] points, with an example of the sequence it reveals (jamming → dark vessels → reported event). It does not contrast itself with plausible siblings like whats_changed, anomalies, or track_history, so an agent must infer the boundary.

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?

Gives useful operational guidance for widening/narrowing ('Raise limit or narrow with lat/lon/hours to drill in') and notes the data source distinction between global and AOI queries. However, it never says when to prefer this over related timeline/diff tools such as whats_changed or track_history, so the choice remains implicit.

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