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get_air_cargo_disruptions

Get real-time air cargo disruption status at major US and international freight hub airports. Returns FAA ground delays, ground stops, arrival and departure delays with estimated minutes, closure status, disruption score, and traffic collapse detection. Covers major cargo hubs including Memphis (FedEx), Louisville (UPS), Anchorage, Chicago O'Hare, Los Angeles, Miami, New York JFK, and Dallas-Fort Worth. Used by air freight forwarders, express carriers, and logistics planners to reroute time-sensitive shipments around airport disruptions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It is quite transparent: it describes the return payload (FAA ground delays, arrival/departure delays, closure status, disruption score, traffic collapse detection) and the geographic scope. It lacks details on update frequency or potential error/empty-result behavior, but for a no-param read-only tool, this is adequate.

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?

The description is four sentences, each serving a distinct purpose: scope, return data, covered airports, and target users. There is no redundancy or filler. It is front-loaded with the core purpose and then provides necessary detail efficiently.

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?

Given the tool has no parameters, no output schema, and no annotations, the description must stand alone. It covers what the tool does, what data it returns, where it applies, and who uses it. This is a complete picture for a no-param data retrieval tool.

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, so the baseline for this dimension is 4. The description does not need to clarify parameter semantics; the schema is trivial (empty properties). No additional parameter information could be added that would meaningfully improve the description.

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 the tool's function with a specific verb and resource: 'Get real-time air cargo disruption status at major US and international freight hub airports.' It lists the exact types of data returned (FAA delays, ground stops, etc.) and names key airports, making it easy to distinguish from sibling tools like rail or port status tools.

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?

The description provides clear context for when to use this tool: 'Used by air freight forwarders, express carriers, and logistics planners to reroute time-sensitive shipments around airport disruptions.' It implies that this is the go-to for air cargo disruptions, but it stops short of explicitly stating when NOT to use it or naming alternatives for other transport modes.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a specific and distinct supply chain intelligence need, such as monitoring port congestion, tracking commodity prices, or analyzing trade policy. While there are multiple tools related to signals and ports, each has a clearly defined purpose (e.g., real-time monitoring vs. trend analysis vs. predictive signals), reducing ambiguity.

Naming Consistency3/5

Most tools use the 'get_' prefix (e.g., get_port_congestion_trends, get_border_delays), but several tools lack it, such as commodity_price_monitor, port_congestion_monitor, and supply_chain_risk_assessment. This mix of 'get_' and non-'get_' naming creates inconsistency. Additionally, some names are noun-heavy (risk_pillar_breakdown) while others are verb-noun (commodity_price_monitor).

Tool Count3/5

At 31 tools, the server is on the higher end of acceptable scope for a comprehensive supply chain intelligence platform. However, some redundancy exists (e.g., multiple signal and port tools), and the count may overwhelm agents without clear prioritization. It is slightly above the ideal range for coherence.

Completeness5/5

The tool set covers the major aspects of external supply chain risk: commodities, transportation (ports, borders, air, rail, chokepoints), manufacturing, macroeconomic indicators, trade policy, natural disasters, and labor actions. It also includes analytical tools like trend analysis and predictive signals, leaving no obvious gaps for its stated purpose of monitoring global supply chain disruptions.

Resources