Get current air quality data for any location. Returns US AQI index, PM2.5, PM10, ozone, NO2, SO2, and CO levels with health category rating. Use this for 'what's the air quality?', 'is it safe to go outside?', 'pollution levels', 'AQI in Los Angeles', 'should I wear a mask?', 'is there smoke in the air?', or any air quality or pollution question.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | City, zip code, or place name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations already declaring readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, the description adds useful behavioral context by specifying that data is 'current' and by disclosing the returned pollutant fields and health category rating. It does not contradict the annotations and provides meaningful output expectations despite the lack of an output schema.
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 front-loaded with the core action and data returned, followed by a useful list of example user intents. The example list is slightly long and somewhat redundant with 'any air quality or pollution question,' but it still earns its place by helping the agent map user phrasing to the tool.
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's low complexity, single required parameter, and rich annotations, the description is largely complete: it states what the tool does, what data it returns, and which user queries should trigger it. It does not discuss data freshness limitations or regional data availability, but these are not critical for a read-only lookup 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 schema already fully documents the sole parameter 'location' as 'City, zip code, or place name,' so the description does not need to add much. The description confirms 'any location' and gives 'AQI in Los Angeles' as an example, but this adds little beyond the schema's existing parameter documentation.
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 action ('Get current air quality data') and resource ('air quality data for any location'), and it enumerates the specific metrics returned (US AQI, PM2.5, PM10, ozone, NO2, SO2, CO). It does not explicitly name or differentiate sibling tools, but it is specific enough that an agent can identify its core purpose.
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 usage context with explicit example queries ('what's the air quality?', 'is it safe to go outside?', 'AQI in Los Angeles') and states it applies to 'any air quality or pollution question.' It does not mention when not to use the tool or point to alternatives, so it stops short of a 5.
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.