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Supply Chain Disruption Monitor

by trevorquinn

search_disruption_news

Search recent news for supply chain disruption signals like strikes, storms, blockages, attacks, or sanctions. Filter results by port or shipping region to identify relevant risks.

Instructions

Search recent news for supply chain disruption signals.

Uses NewsAPI (free tier, 100 req/day — requires NEWS_API_KEY in .env). Returns articles with a 'flagged_high_signal' list highlighting those mentioning strikes, storms, blockages, attacks, or sanctions.

If the query names a known port or shipping region, results are filtered to articles that actually mention that place — this prevents e.g. a Red Sea query returning unrelated Strait of Hormuz coverage just because both mention "attack". Check the 'location_filter' and 'filtered_out' fields in the response to see if/how this applied. Lead each query with the exact place name for the filter to engage.

Example query shapes — lead with the place actually relevant to your route, not necessarily these:

  • " port delay" (e.g. "Rotterdam port delay")

  • " attack blockage" (e.g. "Red Sea attack blockage")

  • " disruption" (e.g. "Suez Canal disruption")

  • "container shipping freight rates" (no location — not filtered)

Args: query: Search query string, ideally leading with a specific place name days: Days back to search (default 7, max 30 on free tier)

Returns: dict with articles list, flagged_high_signal subset, and location_filter/filtered_out showing whether results were filtered

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
queryYes
Behavior5/5

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

With no annotations, the description fully carries the burden and does so thoroughly: it discloses the NewsAPI dependency, rate limit (100 req/day), required API key, the high-signal flagging logic, and the location-filtering behavior with response fields (location_filter, filtered_out). This is rich behavioral context well beyond the raw tool name.

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 somewhat long but front-loaded with the main purpose, followed by useful details, examples, and parameter explanations. Every section earns its place; the example query shapes are slightly repetitive but add value. It is not as tight as the two-sentence get_calls example but still well-structured.

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?

Despite no output schema and no annotations, the description explains the return structure (articles, flagged_high_signal, location_filter/filtered_out) and mentions rate limits. It does not discuss empty-result behavior or error cases, but for a search tool this is adequate coverage.

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

Parameters5/5

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

Schema coverage is 0%, but the description fully compensates by explaining both parameters: 'query' should lead with a place name (with examples), and 'days' gets a default and max value. This adds meaning the schema lacks entirely.

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 states a specific action and resource: 'Search recent news for supply chain disruption signals.' It clearly distinguishes from sibling tools (list_major_ports, get_port_weather, get_vessel_positions, get_port_congestion) by focusing on news, not port/weather/vessel data.

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 concrete usage guidance: asks users to lead queries with the exact place name for location filtering to engage, and gives example query shapes. It also explains when the filter is NOT applied. It lacks explicit alternatives/exclusions, but the sibling tools are clearly different, minimizing the need for such exclusions.

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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