Get full details of a specific NWS weather alert by its ID.
Returns the complete alert including description, instructions, and
affected areas. Use alert IDs from get_alerts results.
Args:
alert_id: The full NWS alert ID (e.g. 'urn:oid:2.49.0.1.840.0.xxx').
| Name | Required | Description | Default |
|---|---|---|---|
| alert_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose with no overlap: get_alerts retrieves alerts by state, get_alert_detail provides details for a specific alert ID, and get_forecast returns weather forecasts by location. The descriptions clearly differentiate their functions, eliminating any potential for misselection.
All tools follow a consistent verb_noun pattern with 'get_' prefix (get_alerts, get_alert_detail, get_forecast). This predictable naming convention makes the tool set easy to understand and navigate without any deviations in style.
Three tools is reasonable for a weather alerts server, covering core functions: listing alerts, getting alert details, and retrieving forecasts. It's slightly minimal but well-scoped; adding tools like historical data or radar might enhance it, but the current count is appropriate for basic operations.
The tool set covers essential weather alert workflows: fetching alerts, accessing details, and obtaining forecasts. Minor gaps exist, such as no tools for historical alerts or broader geographic queries beyond states, but agents can work around these with the provided tools for most common use cases.