Recent earthquakes from the official USGS feed, filterable by magnitude and time — for catastrophe, insurance and logistics risk.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| endtime | No | ||
| starttime | No | ||
| minmagnitude | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does communicate that the data comes from the official USGS feed and that results are recent and filterable, which is useful behavioral context. However, it does not disclose return format, default time range, whether an external API is called, or any rate-limit/staleness details.
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 a single, efficient sentence with zero filler. It front-loads the data source, adds the filtering capability, and closes with relevant use cases. There is no repetition of schema fields or annotation information.
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 no annotations and no output schema, the description needs to do more work to be actionable. It states the source and filters, but does not explain the output shape, default behavior, or return format. An agent could invoke it, but might not know what to expect back or how to interpret the result.
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?
Schema description coverage is 0%, and the description only partially compensates by saying results are filterable by magnitude and time. It does not clarify the meaning of 'limit' or give format details for starttime/endtime, both of which are important for correct invocation. The description adds only coarse parameter context, not per-parameter semantics.
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 what the tool provides: recent earthquake data from the official USGS feed, filterable by magnitude and time. It is not merely a tautology, and it is clearly distinct from the sibling tools, none of which cover earthquakes. It lacks an explicit command verb like 'fetches' or 'lists', which keeps it from a 5.
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 gives an explicit context for use (catastrophe, insurance, and logistics risk), which a helpful but basic guidance. It does not mention alternatives or explicit exclusions, but there are no sibling earthquake tools, so confusion is unlikely. This is clear context without full when-to/not-to guidance.
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 are clearly distinct by resource or function, but fx_rates vs us_treasury_fx and entity_resolve vs lei_lookup could both be plausible choices for the same request. Descriptions generally make the boundaries clear, so the overlap is minor rather than systematic.
All names use lowercase_snake_case, but there is no single naming pattern: tools like btc_network, crypto_price, and weather_forecast are noun phrases, while eu_vat_validate, lei_lookup, and sanctions_screen follow an object+operation style. This mixed convention is readable but noticeably inconsistent.
14 tools is within a reasonable range for a multi-provider data server, and most tools cover a distinct data domain. However, kyb_verify_business heavily overlaps the individual VAT, LEI, sanctions, IBAN, and entity tools, and having two FX rate tools makes the set feel slightly broader than necessary.
The main KYB/entity workflow is well covered, but the set has notable gaps: sanctions screening covers only OFAC and UK OFSi, IBAN validation is structural only, and there are no UBO or adverse-media checks. For the broader data-lookup domain, the coverage is decent but not exhaustive.