Get full detail for a specific earthquake event by USGS event ID. Returns origin, magnitude details, focal mechanism (if available), shake-map link, felt reports, tsunami flag, and impact estimates.
| Name | Required | Description | Default |
|---|---|---|---|
| event_id | Yes | USGS event ID (e.g. 'us7000m5dt'). Get from feed or search results. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful behavioral context by enumerating the specific data groups returned and notes that focal mechanism is included 'if available', which sets expectations about variability in responses.
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 focused sentence that front-loads the tool's purpose and then lists the valuable return dimensions without filler. Every phrase contributes to understanding what the tool returns.
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 single-parameter read-only lookup with rich annotations, the description provides enough return-content detail to guide invocation. There is no output schema, but the enumerated response categories fill that gap reasonably; some ambiguity remains in 'full detail' and 'impact estimates', but this is not a blocking omission.
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 100%, and the schema already explains event_id with an example and guidance to get it from feed or search results. The tool description adds no new parameter-level meaning beyond repeating the notion of a specific earthquake event.
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 opens with a specific verb and resource: 'Get full detail for a specific earthquake event by USGS event ID.' It clearly distinguishes itself from list/search tools like usgs_earthquake_feed and usgs_earthquake_search by emphasizing specificity and the event ID lookup path.
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 clearly implies the right use case: call this when you already have a USGS event ID and need comprehensive event-level details. It does not explicitly name alternatives or state when not to use it, but the phrase 'by USGS event ID' conveys the selection context well.
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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.