airquality

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return values (US AQI, PM2.5, etc.) but doesn't cover important behavioral aspects like rate limits, error conditions, data freshness, or authentication requirements. For a tool with no annotations, this leaves significant gaps in understanding its operation.

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 perfectly concise with two sentences that each earn their place: the first states the purpose and required inputs, the second specifies the return values. There's zero wasted text and it's front-loaded with the core functionality.

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 moderate complexity (2 required parameters, no output schema, no annotations), the description provides adequate but incomplete coverage. It explains what the tool does and what it returns, but lacks information about behavioral constraints, error handling, and other operational aspects that would be important for an agent to use it effectively.

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 description coverage is 100%, with both parameters (latitude and longitude) clearly documented in the schema. The description doesn't add any additional parameter information beyond what's already in the schema, so it meets the baseline expectation when the schema does the heavy lifting.

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 specific action ('Get current air quality conditions') and resource ('for a location'), distinguishing it from the sibling tool 'get_forecast' which presumably provides weather predictions rather than air quality measurements. It specifies the exact data returned, making the purpose 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 usage for obtaining current air quality data at a specific location, but it doesn't explicitly state when to use this tool versus the sibling 'get_forecast' or provide any exclusions or alternatives. The context is clear but lacks explicit guidance on tool selection.

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

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

With no annotations provided, the description carries the full burden. It discloses the return format (hourly data with specific metrics) and implies it's a read-only operation (no destructive hints). However, it lacks details on rate limits, authentication needs, error handling, or data freshness, which are important for a forecast tool.

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 two concise sentences that are front-loaded with the core purpose and return details. Every sentence earns its place by providing essential information without redundancy or fluff.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is reasonably complete. It covers what the tool does and what it returns, but could benefit from more behavioral context (e.g., data source, update frequency) to fully compensate for the lack of annotations and output schema.

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 description coverage is 100%, so the schema already fully documents all parameters (latitude, longitude, days). The description adds no additional parameter semantics beyond what's in the schema, such as explaining coordinate formats or day range implications. Baseline 3 is appropriate when schema does the heavy lifting.

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 specific action ('Get an hourly air quality forecast'), the resource ('for a location'), and the scope ('hourly' with specific metrics: US AQI, PM2.5, PM10). It distinguishes from the sibling tool 'get_air_quality' by specifying it's a forecast rather than current conditions.

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 usage context by specifying 'forecast' and hourly granularity, which suggests when to use this tool (for future predictions) versus the sibling 'get_air_quality' (likely for current conditions). However, it doesn't explicitly state when not to use it or name alternatives, keeping it at a 4.

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