weather_air_qualityCurrent air quality at a lat/lon or US ZIP: US EPA AQI + European AQI, plus pollutant concentrations (PM2.5, PM10, ozone, NO₂, SO₂, CO, dust) in µg/m³, a categorical band, and the primary pollutant. Source: Open-Meteo Air Quality (CAMS + EPA AirNow blend).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | No | ||
| lon | No | ||
| zip_code | No | US ZIP (preferred). Or pass lat+lon. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey a safe, read-only, idempotent operation, so the description does not need to repeat that. It adds meaningful behavioral context beyond annotations by naming the data source (Open-Meteo, CAMS + EPA AirNow blend) and disclosing the exact return payload elements and units, which helps the agent set expectations.
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 dense sentence that front-loads the core resource and location mode, then lists return metrics and the source. Every clause adds useful information and there is no filler or redundant repetition of the schema.
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 there is no output schema, the description does well to spell out the main return fields. However, it does not clarify that a location is required even though the schema lists no required parameters, nor does it mention how this tool relates to nearby weather siblings. These gaps make it only moderately complete for an agent deciding how to invoke it.
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 coverage is only 33%, so the description carries substantial responsibility. It does clarify that the tool accepts either a lat/lon pair or a US ZIP, which adds meaning beyond the bare schema. Yet it stops short of explaining coordinate format, whether location inputs are mandatory despite zero required parameters, or how conflicts between zip_code and lat/lon are resolved.
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 resource as current air quality at a location and enumerates specific output types (AQI, pollutants, band, primary pollutant). It is unambiguous next to weather siblings, though it lacks an explicit verb and does not directly name a competing sibling for differentiation.
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 phrase 'Current air quality' strongly implies this tool is for present conditions and not forecasts or historical analysis, and the location inputs are stated. However, there is no explicit guidance on when to prefer this over weather_current, weather_forecast, or other weather siblings, and no exclusions are mentioned.
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.
Multiple tools have genuinely blurry boundaries: company_change vs company_changes differ only by singular/plural yet serve different purposes, company_domain vs company_classify vs company_lookup_auto all accept a domain, geo_zip_lookup vs geo_enrich vs geo_zip_batch all return ZIP profiles, and email_validate subsumes much of email_disposable and email_free_provider. The domain prefixes help narrow search space, but within many domains an agent cannot reliably predict which tool is the right one.
All 129 tools uniformly follow a snake_case [domain]_[topic] convention (company_, fx_, geo_, dns_, weather_, tax_), which is highly predictable and consistent. Minor deviations include the confusing company_change/company_changes pair, and inconsistent suffix usage (_batch appears on address_validate_batch, company_domains_batch, geo_zip_batch but not on equivalent lookup tools elsewhere).
129 tools far exceeds the 50+ extreem-mismatch threshold, bundling roughly 28 unrelated data domains (weather, fx, tax, ccompany, dns, jobs, flight, email, phone, tax...) into a single MCP surface. Even focusing on one domain forces the agent to load an enormous unrelated tool list; this should be split into many smaller domain-specific servers.
Per-domain coverage is impressively thorough: weather spans current/forecast/hourly/historical/normals/marine/route/air-quality, fx covers rates/convert/historical/volatility/correlation/strenth, and company includes lookup/enrichment/networks/timeline/peer-comparison plus six buyer-tuned signals with profile-introspection tools. Minor gaps like flight being historical-only and smtp probes skipping major email providers are documented scope decisions rather than dead ends.