Get labor actions affecting supply chains extracted from news intelligence. Covers strikes, lockouts, slowdowns, contract negotiations, and protests. Each event includes union, employer, location, worker count, affected ports, status, and impact description. Used by logistics planners and procurement teams to assess labor disruption risk at ports and manufacturing facilities.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the source (news intelligence) and the specific fields returned for each event, making the tool's behavior predictable. It does not cover limitations like refresh rate or geographic scope, but for a get tool with no parameters 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, each adding value: the action, the scope, the data contents, and the use case. It is front-loaded with the main verb and avoids redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no schema parameters and no output schema, the description covers the essential aspects: what it does, what data events contain, and who should use it. It lacks minor details like geographic coverage or data recency, but is otherwise complete for an agent to select the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so parameter explanation is unnecessary. Per the rubric, 0 params earns a baseline of 4; the description adds no param info but none is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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: it retrieves labor actions affecting supply chains. It enumerates the types of labor actions (strikes, lockouts, slowdowns, etc.) and distinguishes itself from sibling tools that cover other disruption types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description identifies the intended audience (logistics planners, procurement teams) and use case (assessing labor disruption risk at ports and facilities), giving clear context. However, it does not explicitly mention alternatives or exclusionary scenarios, so it stops short of a full usage guideline.
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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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.
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.
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.
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.