v1_air_qualityAir quality: US & European AQI plus pollutant concentrations by coordinate. Source: Open-Meteo Air Quality (CAMS). $0.005 per call · GET /v1/air-quality
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Place name (alternative). Example: 'Delhi'. | |
| lat | No | Latitude. Example: '34.05'. | |
| lon | No | Longitude. Example: '-118.24'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, idempotent, open-world, and non-destructive. The description adds useful behavioral context beyond annotations: data source, cost per call, HTTP method, geographic scope, and output content. No rate-limit or auth details are given, but the safety profile is already covered by annotations.
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 compact and front-loaded with the core function, followed by concise source, cost, and endpoint metadata. Each clause adds relevant information, though 'Air quality' somewhat repeats the title.
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 read-only tool with optional parameters and full schema descriptions, the description and schema together provide enough to invoke it correctly. The lack of an output schema is offset by the description naming the return content, though the relationship between 'q' and lat/lon could be clearer.
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 all three parameters with descriptions and examples, so schema coverage is 100%. The description adds only high-level 'by coordinate' context and does not meaningfully enhance understanding of the parameters 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 clearly identifies the tool as returning US & European AQI and pollutant concentrations by coordinate, which is specific and distinguishes it from most siblings. It lacks an explicit action verb like 'retrieves' or 'gets', but the meaning is unambiguous.
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 implies when to use this tool: when air quality data is needed by coordinate or place name. However, it provides no explicit guidance about alternatives, exclusions, or how this relates to sibling tools like v1_weather or geocoding.
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.
Every tool targets a distinct resource or operation—geocoding, weather, DNS, VIN, stock quotes, etc.—with no meaningful overlap in purpose. Even the location- and finance-related tools are clearly separated by their descriptions.
All tools follow the same v1_<resource>[_modifier] snake_case pattern, such as v1_air_quality, v1_reverse_geocode, and v1_validate_email. Although the names are not verb-based, the convention is perfectly consistent across all 31 tools.
31 tools exceeds the 25+ threshold and creates a heavy selection burden for agents, even though the server's stated purpose is broad. Many endpoints are small single-purpose lookups that could be grouped into fewer combined tools without losing clarity.
As a general public-data utility, the set covers a wide range of common lookup categories: location, weather, finance, legal, health, business, internet, and reference data. It has minor gaps like historical financial time series or phone-number validation, but no obvious dead ends since all tools are self-contained read-only lookups.