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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does usefully name the external USGS service, state the US-only constraint, and specify the output units (feet and meters). However, it does not mention error behavior, data availability, precision, datum, or the fact that this is an external network call.
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?
A single, well-constructed sentence delivers the core action, the geographic scope, the data source, and the output units. There is no redundancy or filler.
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 simple two-parameter read-only tool, the description covers the essential context: what is returned, in what units, for what region, and from which service. It does not explain error handling or response structure, but the lack of an output schema is partially mitigated by stating that results include feet and meters.
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 already provides 100% coverage of the lat and lon parameters with detailed descriptions including examples and hemisphere notes. The tool description adds no additional parameter semantics, so the baseline score of 3 is appropriate.
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 names a specific verb ('Get'), a specific resource ('ground elevation'), and constrains the scope ('within the US', 'USGS Elevation Point Query Service'). This clearly differentiates it from the sibling tools, which are about weather, earthquakes, and geocoding.
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 states the US-only scope and the coordinate inputs, but it gives no explicit guidance on when to prefer this tool over alternatives or any when-not-to-use conditions. There is no mention of fallback tools or exclusions.
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 targets a distinct query type: seismic events, elevation, geocoding, weather alerts, current observations, and forecasts. There is no meaningful overlap, and the weather_current/weather_forecast/weather_alerts trio is clearly separated by description.
Tool names are lowercase and simple, with a clear weather_ prefix grouping the NWS-related tools. The main minor deviation is geocode being verb-style while earthquakes and elevation are noun-style, but the overall pattern remains predictable.
Six tools is a well-scoped size for a weather and geo intelligence server. Each tool earns its place and together they cover the major natural query categories without unnecessary overlap or bloat.
The server supports the core workflow of taking a text location, geocoding it, and then getting elevation, current weather, forecast, or earthquake data. Minor gaps like missing reverse geocoding or location-based alert search are workable but not critical.