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 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish this as read-only, idempotent, open-world, and non-destructive. The description adds behavioral detail by stating the response includes a health category rating and specific pollutant levels, which helps the agent set expectations about live, current measurements.
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 tool's purpose and return data, then gives a practical set of example queries. The list of query phrasings is slightly redundant near the end, but overall it is crisp and useful.
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 tool with strong annotations and a complete schema, the description covers what data is returned and the kinds of questions it answers. It does not discuss data freshness, units, or coverage limitations, but these are minor gaps given the tool's simplicity.
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 fully documents the single 'location' parameter as 'City, zip code, or place name' with 100% coverage. The description's examples reinforce that location can be a place name like Los Angeles, but it does not add meaning beyond the schema, so the baseline of 3 is appropriate.
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 uses a specific verb ('Get'), a clear resource ('current air quality data'), and enumerates the exact data returned (US AQI, PM2.5, PM10, ozone, NO2, SO2, CO, health category). It is immediately distinguishable from weather, health, or pollution-related sibling 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 description provides explicit natural-language triggers ('what's the air quality?', 'is it safe to go outside?', 'AQI in Los Angeles') and states to use it for 'any air quality or pollution question.' It does not name alternatives or exclusion cases, but the guidance is clear enough for an agent to select this tool confidently.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.