get_air_quality

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the $0.001 USDC cost via x402, which is a useful behavioral trait beyond the name. However, with no annotations, it does not disclose expected return format, required inputs, or side effects; the read-only nature is only implied.

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 sentence, front-loaded with the tool's purpose and cost. It is compact and readable, with no fluff, though it is arguably too terse to be fully helpful.

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?

With no output schema and no detailed parameter schema, the description must explain what inputs are required and what data is returned. It does neither, leaving the agent to guess how to specify a location or interpret results.

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 has a generic 'params' object with 100% description coverage, which establishes a baseline of 3. Yet the description adds no semantic meaning about what parameters are needed (e.g., location), so it does not genuinely help an agent invoke the tool correctly.

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 states the tool retrieves air quality data and categorizes it under Data & Weather, which makes the purpose immediately understandable. It is distinct from siblings by the specific 'air quality' resource, but does not explicitly contrast with get_weather or get_forecast.

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?

No guidance is provided on when to choose this tool over siblings like get_weather or get_forecast. The only additional context is the monetary cost, which is not a usage guideline.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.