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Recent Disruptions

recent_disruptions
Read-onlyIdempotent

Port-affecting disruption events tracked by PortWatch — tropical cyclones, floods, earthquakes, conflict events — with start/end dates, alert level (GREEN/ORANGE/RED), severity, affected port count, and impacted population. Filter by country, alert level, or year. Use for "what disrupted shipping in the last month", "which port closures hit our supply chain", or to validate a bet on whether a current disruption is escalating.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "country": "Mozambique"
    },
    "name": "recent_disruptions"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearNoFilter to events from this calendar year
limitNoMax events to return (default 25, max 100)
countryNoFilter to a single country name (e.g. "Mozambique")
alert_levelNoRED | ORANGE | GREEN — minimum alert level to include

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "country": "Mozambique"
      +  },
      +  {
      +    "alert_level": "RED",
      +    "limit": 10,
      +    "year": 2024
      +  }
      +]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds behavioral context: lists event types and filter options, which is consistent with annotations. No contradictions.

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

Conciseness5/5

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

Description is two sentences, front-loaded with key information (event types, attributes, filters, use cases). Every sentence adds value with no redundancy.

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?

No output schema, but the description explicitly lists return fields (start/end dates, alert level, severity, affected port count, impacted population) and provides filter examples. Covers the essential information for an agent to understand the output.

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 coverage is 100%, so the parameters are fully described in the schema. The description provides examples but does not add new semantic details beyond the schema. Baseline 3 is appropriate.

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 it returns port-affecting disruption events with specific attributes (start/end dates, alert level, severity, etc.). It distinguishes from siblings like recent_alerts and chokepoint_status by specifying the types of events tracked (tropical cyclones, floods, etc.).

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?

Provides explicit use cases like 'what disrupted shipping in the last month' and 'which port closures hit our supply chain'. Does not explicitly state when not to use or list alternatives, but the context signals and sibling list offer differentiation.

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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