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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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently states that observations are 'extracted from news intelligence' and includes the specific data fields returned (direction, magnitude, trade lane, rate values, confidence score, source URL). While it does not explicitly mention read-only status, the verb 'Get' implies it, and the description adds value by explaining the source and contents beyond a simple 'get' operation.
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 earning its place: the first states the core function, the second details coverage and content, and the third gives the use case. It is front-loaded with the key verb and resource, and there is no redundant or filler text. This is a model of concise yet informative writing.
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 tool has no parameters and no output schema, the description provides all necessary context: what it does, what data it returns, and why it would be used. It covers the indexes, the observation fields, confidence scores, and source URLs, making it complete for an agent to select and invoke the tool correctly. No additional information is needed for a 0-parameter get 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 the baseline for this dimension is 4. The description adds no parameter information (correctly, since none exist), but it enriches the tool's semantics by describing the output attributes (direction, magnitude, etc.), which is helpful given the absence of an output schema. This meets the baseline without needing compensation for low schema coverage.
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 identifies the tool's function as retrieving freight rate index observations from news intelligence, listing the specific indexes covered (BDI, SCFI, WCI, CCFI, HARPEX). This distinguishes it from sibling tools like get_freight_rate_pressure, which focuses on pressure rather than observations. The verb 'get' is specific and the resource is well-defined.
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 clear use context by stating it is 'Used by logistics planners to track rate trends and identify cost pressure signals.' It does not explicitly name alternative tools or say when not to use it, but the use case and content description are sufficient to guide an agent in appropriate scenarios. This aligns with a 'clear context, no exclusions' rating.
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.
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.
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).
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.
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.