Currently-active hurricanes and tropical systems from NOAA NHC, with category, wind/pressure, current position, movement, and forecast cone link.
| Name | Required | Description | Default |
|---|---|---|---|
| basin | No | Optional basin filter: 'AL' (Atlantic), 'EP' (Eastern Pacific), 'CP' (Central Pacific). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds behavioral value by specifying that data is 'currently-active' (not historical) and names the authoritative source, NOAA NHC.
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 one efficient, front-loaded sentence. It names the subject, source, and output fields without filler, so every part contributes useful 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?
For a simple tool with one optional parameter and no output schema, the description covers the main return content: category, wind/pressure, current position, movement, and forecast cone link. It does not describe response formatting or empty/no-active-storm behavior, but these are minor gaps given the low complexity.
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 already documents the sole optional basin parameter with clear value explanations ('AL', 'EP', 'CP'). With 100% schema description coverage, the description need not repeat parameter details; it adds no parameter-specific meaning beyond the schema.
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 identifies the resource and scope clearly: currently-active hurricanes and tropical systems from NOAA NHC, with specific data fields. It lacks an explicit verb like 'list' or 'get', but the noun-phrase framing is unambiguous and distinguishes it from generic weather or alert tools.
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 scope is implied by 'currently-active hurricanes and tropical systems from NOAA NHC', so an agent can infer when it is relevant. However, there is no explicit guidance about when to prefer this over siblings such as nws_active_alerts or weather_current, nor any exclusions for historical or non-hurricane requests.
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.