NWS Weather Alerts

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

No annotations are provided, so the description carries the burden. It reveals that the response includes description, instructions, and affected areas, which is useful. However, it does not mention error behavior, absence of side effects, or rate limits, leaving some transparency gaps for a read 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 concise and front-loaded with the main purpose. The args section adds a small redundancy with the schema but is acceptable. Overall, it is well-structured and free of waste.

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 simplicity (one parameter) and the presence of an output schema, the description provides sufficient context: what the tool does, where the input comes from, and what the return includes. A minor gap is the lack of explicit mention of read-only status or error handling, but these are inferable.

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 only states alert_id is a string, but the description adds critical meaning by specifying it is 'The full NWS alert ID' and providing an example format. This significantly compensates for the 0% schema description coverage.

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 begins with 'Get full details of a specific NWS weather alert by its ID', which clearly states the verb, resource, and scope. It distinguishes itself from sibling tools by emphasizing 'specific' and referencing IDs from get_alerts results.

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?

It provides clear guidance on when to use the tool: after obtaining alert IDs from get_alerts. While it doesn't explicitly say when not to use it, the context implies it is for detailed lookups rather than listing or forecasting, which is sufficient.

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, the description carries full burden. It explicitly states that results are 'current weather alerts including watches, warnings, and advisories' and mentions the active-alert scope. It also explains filtering by severity and event, providing useful behavioral context beyond a simple read 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 succinct and well-structured. It front-loads the purpose in the first line, adds a brief output summary, and then presents a compact, dense Args block. No redundant or ambiguous wording.

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 that an output schema exists (indicated in context), the description need not explain return values. It thoroughly covers parameter semantics, default behavior, and the scope of results. The description is complete enough for an agent to select and use the tool correctly without additional information.

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 0%, so the Args section is crucial. It provides concrete examples for state ('CA', 'TX', 'NY'), exact allowed severity values, example event types, and the default/max limit. This goes far beyond the bare schema and gives agents everything needed to construct valid calls.

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?

Description opens with 'Get active NWS weather alerts for a US state,' using a specific verb and resource with clear scope. This distinctly separates it from sibling tools like get_alert_detail (single alert) and get_forecast (weather forecasts).

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 clearly implies when to use this tool: for retrieving active state-level alerts. It does not explicitly name alternatives or state when not to use, but since sibling tools serve different purposes, the usage context is clear. Could be improved by adding an explicit exclusion note.

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 of behavioral disclosure. It explains the data source (National Weather Service), the forecast type (multi-day), and geographic constraints, but omits potential behaviors like error handling for invalid coordinates, data update frequency, or specific return format. This is adequate but not rich.

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 concise and well-structured: a purpose statement, a sentence on scope/source, and a clear Args section. Every sentence earns its place without redundancy. It is front-loaded with the core verb and resource.

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?

The tool is simple (2 numeric parameters) and has an output schema, so return value documentation is not needed. The description covers purpose, geographical scope, and parameter examples, making it complete for typical use. It lacks details on error behavior or units, but these are not critical for a straightforward forecast tool.

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 provides only type and title for latitude and longitude, with zero description coverage. The tool description compensates fully by explaining each parameter with concrete examples (e.g., 38.8894 for Washington DC), adding meaning well beyond the schema. The Args section is directly informative.

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') and resource ('weather forecast') with clear location parameters, distinguishing it from sibling tools like get_alerts and get_alert_detail, which focus on alerts. The scope is explicitly stated as a multi-day National Weather Service forecast, providing a precise purpose.

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 clearly states the tool works only for locations within the United States and its territories, providing an implicit exclusion for international locations. It does not explicitly contrast with sibling alert tools, but the purpose is so distinct that usage context is clear. No explicit 'when not to use' guidance is given.

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