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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

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description fully discloses what the tool returns: FAA ground delays, ground stops, delay minutes, closure status, disruption score, and traffic collapse detection. This gives strong insight into the tool's behavior without overexplaining. It doesn't mention potential limitations like update frequency, but that's not a major gap.

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 three focused sentences: purpose, data returned, and use case. No redundant or vague wording. Every sentence adds value, and it is front-loaded with the core verb and resource.

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?

Given the tool's moderate complexity and absence of output schema, the description covers return fields, airport coverage, and target users. It is slightly overbroad in claiming 'international' airports while listing only US hubs, but overall it is sufficiently complete for a 0-parameter 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 there are no parameter semantics to clarify. Per rubric, the baseline is 4 for no-parameter tools. The description adds context about scope and content, though it doesn't need to explain params.

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 uses a specific verb ('Get') with a clear resource: real-time air cargo disruption status at major freight hub airports. It names specific airports and distinguishes itself from sibling tools focused on other transport modes (rail, border, port).

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: it is used by air freight forwarders, express carriers, and logistics planners to reroute shipments around airport disruptions. It does not explicitly name alternatives or exclusions, but the specialized scope makes the intended use obvious.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes targeting specific supply chain dimensions like commodity prices, port congestion, or manufacturing indicators, with clear boundaries. However, some overlap exists between tools like 'get_commodity_volatility_alerts' and 'commodity_price_monitor', which both focus on commodity price changes, potentially causing confusion in tool selection.

Naming Consistency4/5

Tool names follow a consistent 'verb_noun' pattern (e.g., 'get_action_signals', 'get_air_cargo_disruptions'), with minor deviations like 'commodity_price_monitor' and 'manufacturing_output_indicator' using noun-based naming. This maintains readability but slightly breaks the overall convention.

Tool Count2/5

With 25 tools, the count feels excessive for a single server, likely overwhelming users and agents. The server covers a broad domain, but many tools could be consolidated (e.g., multiple commodity-related tools) to reduce complexity and improve focus.

Completeness5/5

The tool set provides comprehensive coverage of the supply chain domain, including risk assessment, real-time monitoring, predictive analytics, and executive reporting. It supports full lifecycle management from data retrieval to actionable insights, with no obvious gaps in functionality.

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