Get freight rate index observations extracted from news intelligence. Covers major ocean freight indexes (BDI, SCFI, WCI, CCFI, HARPEX) with direction, magnitude, trade lane, and rate values. Each observation includes confidence score and source URL. Used by logistics planners to track rate trends and identify cost pressure signals.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that observations include confidence score and source URL, and mentions data origin ('extracted from news intelligence'). However, it does not disclose update frequency, data freshness, pagination, or any limitations. The behavioral traits are partially transparent but not comprehensive.
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 three sentences, each adding value: the primary function, the specific indexes and fields, and the target user/use case. It is concise, front-loaded with the verb, and contains no filler. Every sentence earns its place.
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?
Given the absence of an output schema, the description does explain return values (direction, magnitude, trade lane, rate values, confidence score, source URL). However, it omits any mention of the time range or history covered, which is relevant for a data retrieval tool. Otherwise, it is complete for a no-parameter 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 input schema has zero parameters, so schema coverage is trivially 100%. Per guidelines, a 0-parameter tool gets a baseline of 4. The description adds no parameter-specific semantics, but none are needed. It suffices that the tool returns a fixed dataset.
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 verb 'Get' and the resource 'freight rate index observations', and lists specific major ocean freight indexes (BDI, SCFI, WCI, CCFI, HARPEX). This specificity distinguishes it from sibling tools like commodity_price_monitor and get_air_cargo_disruptions, though it does not explicitly contrast with get_freight_rate_pressure.
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 provides a use case: 'Used by logistics planners to track rate trends and identify cost pressure signals.' However, it lacks explicit guidance on when to use this tool versus alternatives like get_freight_rate_pressure or get_freight_transportation_index. It implies usage context but does not offer exclusions or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.