Weather & Geo Intel MCP

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

With no annotations, the description takes on the full disclosure burden; it does reveal the data source, output fields, and available filters, which is solid. It does not discuss update cadence or default behavior, and the unsupported bounding-box claim is a behavioral inaccuracy that prevents a higher score.

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?

Two tight sentences with the main verb and resource front-loaded, followed by filter and return-value detail. The structure is efficient, though the inaccurate 'bounding box' phrase is an avoidable addition.

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 no-output-schema tool, the description names the returned fields and the filters, and the schema supplies defaults and parameter meanings. However, the claimed bounding-box option is left completely unspecified in the schema, and units/ordering of results are not addressed, so the definition falls short of fully complete.

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 parameter baseline is 3 and the schema already documents each property. The description maps 'look-back window' to days and 'point+radius (km)' to lat/lon/radius_km, but the mention of a bounding box has no corresponding properties, creating a misleading parameter expectation.

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 action (list), the resource (recent earthquakes from the USGS feed), and the domain, which is sharply distinct from the weather/elevation/geocode siblings. It loses a point because it advertises 'optionally a bounding box' even though the input schema has no bounding-box parameters.

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 use case is explicit: call this when you need recent earthquake data worldwide, and the sibling tools are unrelated enough that no exclusion list is necessary. There are no prerequisites or cautionary conditions stated, but none are obviously required for this read-only feed.

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 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.

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

With no annotations provided, the description carries the full behavioral burden. It usefully discloses the US Census data source, the fact that a city/town name returns the centroid, and the US-only scope. It could add more detail about failure modes or coordinate ordering, but the core behavior is transparent enough for a single-parameter 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?

Three sentences, front-loaded with the core purpose, followed by concrete examples and usage context. Every sentence adds value, and there is no redundant or filler text.

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 one-parameter tool with no output schema, the description covers accepted input forms, geographic scope, output type, and downstream usage context. Slight gaps remain around error handling and coordinate format, but these are not critical for an agent selecting and invoking the 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 input schema covers the parameter at 100%, so the baseline is 3. The description adds meaningful semantic detail beyond the schema by explaining that a place name resolves to the city centroid and by reinforcing the two accepted input forms with examples. This extra context helps an agent understand what values are valid.

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 a specific verb ('Convert') and resource ('US street address OR city/town name') with a concrete output ('latitude/longitude coordinates'). It also distinguishes itself from sibling location-related tools (weather, elevation, earthquakes) by positioning geocode as the coordinate lookup step.

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 explicit when-to-use guidance: 'Call this first whenever you have a location as text and need coordinates for the weather or elevation tools.' It also provides a clear exclusion with 'US locations only,' telling the agent when not to use it.

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 burden of disclosing behavior. It states this is a listing/read operation, restricts scope to active alerts, and lists the returned fields. Minor ambiguity remains around the 'marine area' mention conflicting with the two-letter state code instruction.

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?

Three concise sentences, each adding value: the tool's purpose, the required input format, and the expected return fields. No filler or redundant wording; the most important information is front-loaded.

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 single-parameter read-only tool with no output schema, the description provides enough to invoke it correctly and understand the response shape. The only real gap is the unresolved 'marine area' usage, which could mislead an agent into passing a marine zone identifier rather than a state code.

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 coverage is 100%, so the schema already documents the 'area' parameter as a two-letter US state/territory code. The description adds useful examples (CA, TX, FL) and mentions marine areas, but does not substantially go beyond the schema or clarify the marine-area format.

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 a specific verb and resource: 'List active NWS weather alerts' for a US state or marine area. It also enumerates what the alerts include (warnings, watches, advisories) and what the response contains, making it easily distinguishable from siblings like weather_current and weather_forecast.

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 for when to use the tool: to retrieve active NWS alerts. It provides concrete input examples and specifies the geographic scope, but it does not explicitly contrast this tool with sibling tools such as weather_forecast or weather_current.

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?

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose that the operation is read-only, the data source (NWS), and the output fields. However, it does not mention units, staleness, error behavior, or what happens when no station is near, which are relevant but not critical for invocation.

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 front-loaded sentence that packs in the verb, resource, scope, source, and outputs without any filler or redundant phrasing.

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 read-only tool with two well-documented parameters and no output schema, the description is nearly complete: it explains what it returns and from where. It lacks units and edge-case behavior, but these are not required to invoke the tool correctly.

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%, and both lat and lon already have detailed descriptions, so the baseline is 3. The description adds no new parameter-level meaning beyond repeating the US location constraint already present in the schema.

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 clear resource ('latest observed weather conditions'), and a specific scope ('US location by latitude/longitude, from the nearest NWS observation station'). It also enumerates the returned fields, making it immediately distinguishable from sibling tools like weather_forecast (observed vs forecast) and weather_alerts.

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 its use case: current observed weather for a US lat/lon. However, it does not explicitly name alternatives or state when not to use it, leaving the agent to infer the difference from weather_forecast based on the word 'observed' and the tool name.

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 the behavioral burden. It discloses that this is a read operation ('Get'), US-only coverage, NWS source, and the response contents (named periods, temperature, wind, short/detailed text). It does not mention limitations such as exact forecast horizon or units, but the essential behavior is transparent.

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?

Three short sentences, each earning its place: core action, expected return shape, and routing hint. No redundant filler or repetition of schema details.

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 low-complexity, read-only forecast tool with three parameters and no nested objects, the description plus fully covered schema gives an agent everything needed to select and call it correctly. The return content is described even though no output schema exists.

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 coverage is 100%, so lat/lon already have decimal-degree and coverage context, and hourly has a default and behavior note. The description adds no parameter-level detail beyond the schema, so the baseline 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?

States a specific verb ('Get'), resource ('multi-day weather forecast'), and required inputs (US lat/lon), and identifies the data source (National Weather Service). The return summary (named periods with temperature, wind, and short/detailed forecast) makes it easy to distinguish from siblings like weather_current and weather_alerts.

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?

Gives explicit routing guidance: use geocode first if only an address is available, which clearly states an important precondition. It does not explicitly name weather_current as the alternative for current conditions, though 'multi-day forecast' implies the distinction.

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