solarstorm

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

With no annotations provided, the description carries the behavioral disclosure burden. It does disclose the output contents and the 24-hour horizon, which is useful. But it does not define what the 'verdict' means, how the forecast is derived, or any edge-case behavior, so transparency is only partial.

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?

One dense, well-ordered sentence front-loads the purpose and lists the output components naturally. Every clause earns its place with no filler or repetition.

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 lookup with no output schema, the description names all three return elements and the input convention, so an agent has enough to call it correctly. Minor gaps—such as verdict semantics and latitude range—keep it from being 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 coverage is 0%, so the description must compensate. It does by explaining that latitude is in geographic degrees and gives a concrete example (48.2 for Vienna), while also tying it to the output. It omits the valid range and sign convention, but for a single common parameter this is adequate.

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 deliverable (an aurora visibility outlook) and its three components: needed Kp, expected max Kp in 24 hours, and a verdict. This clearly distinguishes it from siblings like get_current_kp or get_kp_forecast_3day, which are general instead of latitude-specific.

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 intended use is implied: call this when you need a location-specific aurora visibility outlook rather than raw Kp readings or a general forecast. However, the description never explicitly says when to use this over the sibling tools, leaving the agent to infer the boundary.

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 present, so the description carries the disclosure burden. It conveys the key behavioral output: the latest value, severity level, and 24-hour maximum. It does not describe update frequency or potential observation delay, but for a simple zero-parameter read the core behavior is clear.

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 compact sentence with no filler. It front-loads the resource name and directly enumerates the useful output fields.

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 zero-parameter tool with no output schema, the description fully covers what an agent needs to understand the return value: latest Kp value, severity level, and the 24-hour observed maximum. No additional context is necessary to call this 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?

The tool has zero parameters, so there is no parameter meaning for the description to add. The baseline of 4 applies because no parameter documentation is needed.

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 identifies the resource ('current planetary Kp index') and the exact returned content: latest value, level category, and observed 24-hour maximum. The word 'current' and 'observed' distinguish it from the forecast-oriented sibling get_kp_forecast_3day.

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 that this is for accessing the current measured Kp index rather than forecasts, and the observed 24-hour max clarifies the temporal scope. It does not explicitly state exclusions or name alternative tools, but the current-vs-forecast contrast provides clear context.

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 mentions the source (official NOAA) and the output semantics (maximum expected Kp per day, Europe/Berlin days), which provides some insight. However, it does not describe the response format, update frequency, or any limitations. For a simple tool with no parameters, this is acceptable but not comprehensive.

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, information-dense sentence with zero redundancy. Every element — the source, the time frame, the metric, and the timezone — earns its place, and the key attribute (3-day forecast) 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?

Given the tool's simplicity (no parameters, no output schema, no nested objects), the description covers the essential context: it is a forecast, the metric is Kp, and the days are Europe/Berlin. It does not need to explain return values since there is no output schema, and the absence of parameters means no parameter documentation is required. The only minor omission is a note on the forecast's update cadence, but that is not critical for correct invocation.

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 tool has zero parameters, so there is nothing for the description to explain. Per the guidelines, a score of 4 is the baseline in this case, and since the description correctly focuses on the output rather than parameters, it fully satisfies this dimension.

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 it provides an 'Official NOAA 3-day geomagnetic forecast' and specifies the exact data: 'the maximum expected Kp per calendar day'. This is a specific verb-resource pair that distinguishes it from siblings like get_current_kp (current conditions) and get_solar_flares_24h (solar flares), making its purpose unambiguous.

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 usage by its focus on a 3-day forecast, but it does not explicitly state when to choose this tool over its siblings or when not to use it. For instance, it doesn't mention that get_current_kp is for real-time Kp values. The guidance is adequate but left to the agent's inference instead of being explicit.

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?

There are no annotations, so the description carries the full burden. It discloses the data source (GOES), the time window (past 24 hours), and the returned concepts (current and peak flare class), but it does not mention data freshness, update cadence, or any limitations. This is acceptable for a simple read-only tool but leaves some behavioral context unstated.

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 immediately identifies the topic, source, time window, and outputs. Every element earns its place, with no repetition 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 zero-parameter, no-output-schema retrieval tool, the description is complete: it names the source, the time range, and exactly what values the agent should expect ('current flare class' and '24-hour peak class'). No additional context is necessary for a correct call.

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 tool has zero parameters, so the schema carries no parameter burden. The baseline for zero-parameter tools is 4, and the description does not need to explain parameter semantics because none exist.

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 resource ('Solar X-ray activity of the past 24 hours from GOES') and the specific outputs ('current flare class and the 24-hour peak class'). Its subject matter is distinct from the siblings, which focus on aurora and Kp indices, so an agent can differentiate it without inspecting other tools.

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 its use: it is for solar X-ray/flare data over the past 24 hours, not aurora or Kp forecasts. It does not explicitly name alternatives or state when not to use it, but the domain and time window are unambiguous enough for selection among the listed siblings.

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