Get the ground elevation at a latitude/longitude within the US, from the USGS Elevation Point Query Service. Returns elevation in feet and meters.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude in decimal degrees, e.g. 39.5883. US coverage only (USGS). | |
| lon | Yes | Longitude in decimal degrees, negative in the western hemisphere, e.g. -105.6438. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure. It states both the return unit types (feet and meters) and the data source, which are meaningful behavioral details. It does not disclose edge-case behavior such as out-of-coverage coordinates or service errors, but for a simple read-only lookup this is not a major gap, so a 4 is warranted.
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 with no fluff; the verb and resource are front-loaded, and the return information is appended efficiently. Every phrase earns its place.
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 two-parameter lookup with complete schema documentation, the description is complete: it states the operation, geographic scope, source, and return units. Since there is no output schema, the mention of feet and meters is essential and present, so nothing an agent needs to call it correctly is missing.
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 already documents both parameters with type, examples, and US-coverage context, and schema coverage is 100%. The description adds no additional parameter semantics beyond what the schema provides, so the baseline of 3 applies.
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 the specific verb 'Get' and a clear resource ('ground elevation at a latitude/longitude within the US'), names the underlying source (USGS Elevation Point Query Service), and the tool is unmistakably distinct from sibling weather, geocode, and earthquake tools. There is no ambiguity about what the tool does.
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 gives clear context by restricting use to the US and identifying the USGS service, which tells an agent when this is the right tool. It does not explicitly name alternatives or state when-not-to-use, but sibling tools cover unrelated domains, so the usage context is sufficient without an explicit exclusion.
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 maps to a distinct data source and purpose: geocode converts addresses, elevation samples terrain, earthquakes returns seismic events, and the weather_* tools clearly separate alerts, current observations, and forecast. No two tools would plausibly return the same data.
All names are lowercase snake_case and descriptive, with weather_ prefix clearly grouping NWS tools. geocode is a verb-like single word rather than a noun/resource, and earthquakes/elevation lack the weather_ prefix, so there are minor deviations from a fully uniform pattern.
Six tools is well-scoped for a US-focused weather and environmental intel server. Each tool covers a separate dataset with no redundant utilities, while keeping the surface small enough for an agent to navigate easily.
The server covers the essential US weather workflows: geocoding from an address, current conditions, multi-day forecast, and alerts, plus related elevation and earthquake data. No critical gaps are implied by the stated purpose and tool surface.