Database statistics plus per-dataset freshness: how recently each dataset was refreshed by our pipeline, how old its newest record is, and whether it is ok / lagging / stale / empty. Call this to decide how much to trust anything else this server returns.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 the tool returns freshness information and status categories (ok/lagging/stale/empty), implying a read-only query. However, it does not mention performance characteristics, rate limits, or whether the tool might affect server state, though for a tool with zero parameters, the disclosure 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 two sentences: the first describes the output in detail, and the second gives a clear usage directive. Every word adds value, there is no repetition or fluff, and the most important information (what it returns and when to call it) is front-loaded.
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 zero parameters and no output schema, the description adequately covers what the tool returns (per-dataset freshness and status) and why to use it. It could be more explicit about the return structure (e.g., whether it returns a list or dict), but the mention of specific fields like 'how recently refreshed' and 'status categories' provides sufficient context for an agent to call and interpret results.
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 0 parameters, and schema coverage is trivially 100%. The description doesn't need to explain parameters, and it does not attempt to describe non-existent ones. According to the rubric, zero parameters earns a baseline of 4, which is appropriate here.
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 it provides 'database statistics plus per-dataset freshness' and lists specific outputs (refresh recency, record age, status categories). It distinguishes itself from sibling tools by positioning itself as a diagnostic tool for assessing data trustworthiness, whereas siblings like shipping_fleet or shipping_oil_prices focus on specific data domains.
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 explicitly instructs the agent to 'Call this to decide how much to trust anything else this server returns,' providing a clear use case. It does not explicitly list when not to use it or name alternatives, but with 0 parameters and a distinct purpose, the guidance is sufficient.
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, but there is some overlap between shipping_status and shipping_freshness (both provide freshness information) and between shipping_sanctions and shipping_sanctioned_at_eu (both deal with sanctions, though one is general and the other EU-specific). Descriptions help disambiguate, but the potential for confusion remains.
All tools follow a consistent 'shipping_' prefix followed by a descriptive noun phrase (e.g., shipping_chokepoint, shipping_emissions, shipping_oil_trade). The naming pattern is uniform and predictable, with no mixed conventions.
With 13 tools, the server covers a broad domain (chokepoints, trade flows, emissions, fleet, sanctions, document intelligence, oil data) without being overwhelming. Each tool serves a clear, non-redundant purpose, and the count is well-scoped for a specialized maritime intelligence server.
The tool set covers core maritime intelligence areas: chokepoint monitoring, trade flows, emissions, fleet, sanctions, oil prices, and data freshness. Minor gaps exist (e.g., no tool for port congestion or vessel tracking outside chokepoints), but the core workflows for sanctions, trade, and emissions are well-covered.