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

With no annotations provided, the description carries the full burden and does a good job by stating that this is a read-only 'Get' operation and by specifying the exact return format. The only notable omission is timezone awareness, since 'current time' is not qualified as local or UTC.

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 brief, front-loaded with the core purpose, and every sentence adds useful information. The return format is specified without unnecessary 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 read-only utility tool with an output schema and an explicitly documented return format, the description is fully sufficient. No additional usage context or edge-case guidance is necessary.

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 about arguments. The baseline of 4 applies, and the description appropriately focuses on the return value instead.

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 a specific verb ('Get') and names the exact resource: a human-readable string of the current time, weekday, and date. It is immediately distinct from the weather-focused sibling 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 purpose is clear enough that an agent knows to use it whenever the current date/time is needed, and the sibling names make it obvious this is the only non-weather tool. It does not explicitly state exclusions or alternative conditions, but no meaningful alternatives exist here.

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?

The description discloses the time offset constraints (min 2 hours, max 121 hours) and the return unit (millimeters per second), which is useful. However, with no annotations provided, the description carries the full burden. It does not mention potential errors (e.g., invalid location, out-of-range offset), data source, or whether the value is an instantaneous rate or averaged. This is adequate but not rich.

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 well-structured with a clear summary, parameter explanations, return type, and examples. It is slightly verbose with the examples taking up space, but they are genuinely helpful for understanding the offset semantics. Every section 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?

The tool has an output schema, so return values are documented. The description covers the two required parameters, their semantics, and constraints. It lacks edge-case handling (e.g., what happens for invalid locations or out-of-range offsets) and does not mention data source or accuracy, but for a simple lookup tool it is largely 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 0%, so the description must compensate. It does: it explains the 'location' parameter with an example ('Zurich') and the 'lead_time_swiss' parameter with a detailed definition ('Hour offset from today at 00:00 Swiss local time') plus min/max bounds and examples. This adds significant meaning beyond the bare 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 clearly states the tool's function: 'Get precipitation rate for a location at a specific offset time.' It specifies the resource (precipitation rate), the location, and the time offset. It is distinct from siblings like total_rainfall or snow_depth, which measure different weather variables.

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 provides clear context on when to use the tool: for precipitation rate at a specific hour offset. It includes examples that illustrate usage. However, it does not explicitly state when not to use it or name alternatives (e.g., total_rainfall for accumulated precipitation), so it falls short of a 5.

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 behavioral burden. It discloses non-obvious constraints: lead_time_swiss is an hour offset from 00:00 Swiss local time, min/max valid values are 2 and 121 hours, and the return value is in Pascals. This is valuable context beyond a simple 'get pressure' statement.

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 front-loaded with purpose and then compactly documents parameters, return type, and examples. Every part earns its place, especially the examples that disambiguate the offset semantics.

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 read tool, the description is complete: both parameters are fully specified, return units are stated, and examples cover multiple offset ranges. The output schema covers the return shape, so nothing essential is missing 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?

Schema coverage is 0%, so the description must compensate, and it does thoroughly. location is explained with an example, and lead_time_swiss gets a precise meaning, valid range, and illustrative examples mapping offsets to Swiss local times.

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 a specific verb and resource: "Get sea-level pressure for a location at a specific offset time." This clearly differentiates it from sibling weather-variable tools like temperature, wind_speed, or total_rainfall, even without naming them.

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 purpose is clear enough to infer when to use the tool, but there is no explicit 'use this when' guidance or mention of alternatives/exclusions. The agent must infer selection from the purpose statement and sibling tool names.

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 disclosure burden. It states the return type and units (float in meters), the exact meaning and valid range of lead_time_swiss (2 to 121 hours from 00:00 Swiss local time), and provides multiple examples that clarify the time arithmetic. This goes well beyond minimal behavior disclosure.

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 compact and well-organized with explicit Args, Returns, and Examples sections. It front-loads the core purpose, then gives parameter details and illustrative examples without 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?

Given no annotations, a bare input schema, and a simple two-parameter tool, the description provides everything an agent needs: parameter meaning, constraints, return units, and example calls. The sibling tools are different variables and require no additional disambiguation.

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 0%, so the description must fully document parameters. It explains location with an example, defines lead_time_swiss as the hour offset, provides min/max constraints, and all examples map offsets to concrete Swiss local times. Every parameter is semantically covered despite the bare 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 opens with a specific verb-resource pair: 'Get forecasted snow depth for a location at a specific offset time.' It clearly distinguishes snow_depth from the sibling weather tools by naming the exact physical quantity and its temporal dimension.

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 makes clear it is for obtaining forecasted snow depth at a location and offset time, and documents the valid lead-time range. It does not explicitly compare with alternatives, but among the listed sibling weather tools, selection is straightforward from the variable name and description, so explicit exclusion is not necessary.

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 disclosure burden. It conveys a non-mutating read/prediction ('Get ... Predicted sunshine hours'), states the valid offset bounds (2–121 hours), and anchors the time base ('today at 00:00 Swiss local time'). It stops short of describing failure behavior for invalid ranges, so a perfect score is not 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 docstring is organized into Intro, Args, Returns, and Examples, with the core operation front-loaded. The five examples are slightly redundant but each earns its place by clarifying the offset semantics; no filler is present.

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 tool with three required parameters and no annotation context, the description is complete: it covers every parameter, the return type, the timezone/offset convention, and valid bounds. The output schema exists and the Returns line already states the float result, 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?

Schema description coverage is 0%, so the description must supply all parameter meaning, and it does: it defines location with an example, defines both offsets as hours from today 00:00 Swiss time, and gives min/max constraints. The examples additionally map numeric ranges to natural-language periods such as 'today' and 'tomorrow morning.'

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 opens with a specific verb-resource pair ('Get sunshine hours') and immediately scopes it by 'location and offset period.' This target variable is distinct from sibling weather tools such as total_cloud_cover and temperature, and the examples reinforce what the tool is for.

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 Args section and five examples provide clear context for when to call this tool: to obtain forecast sunshine hours over a specific lead-time window, expressed in Swiss local time. It does not explicitly name alternatives or exclusion conditions, but the resource it returns makes the appropriate choice obvious among the weather-variable siblings.

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 bears full responsibility for behavioral disclosure. It does mention that the return is 'Maximum air temperature in Celsius' and provides min/max lead time limits, which is helpful. However, the wording 'Maximum' is confusing and seems to contradict the examples (which show temperatures at specific hours), and it does not describe error handling, data source, or any side effects. This is a partial disclosure with notable gaps, so a 3 is appropriate.

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 well-structured as a docstring with a one-sentence purpose, Args, Returns, and Examples. It is front-loaded with the core purpose and uses minimal words to convey parameters and return type. Each part earns its place, and the examples are concise. No redundancy or fluff.

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 (2 params, returns a float) and the presence of an output schema, the description covers the essentials: what it does, parameter semantics, return type, and examples. It explains the lead time offset fully and even notes limits. The main gap is the ambiguous 'Maximum' wording and lack of error handling or data-source context, which slightly incomplete for a fully specified tool. However, it is largely sufficient, so a 4 is suitable.

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 provides only parameter names and types with no descriptions (0% coverage). The description fully compensates by explaining both parameters in detail: 'location' as a name and 'lead_time_swiss' as an hour offset with explicit min/max values and an explanation of the base time (today at 00:00 Swiss local time). The examples illustrate the mapping between offset and actual time, adding significant semantic value beyond 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 begins with a clear verb and resource: 'Get air temperature for a location at a specific offset time.' This distinguishes it from sibling tools like total_cloud_cover or wind_speed, which address different weather parameters. The purpose is unambiguous and it immediately tells an agent 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 clearly states the tool fetches air temperature, which implicitly indicates when to use it (when temperature is needed), but it does not explicitly mention when not to use it or offer alternatives. It provides context on the offset and time zone, but no exclusions or comparisons to sibling tools. This meets the 'clear context, no exclusions' bar, so a 4 is warranted.

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 burden of behavioral disclosure. It states the input constraints (min/max offset of 2 to 121 hours), the return type (float), and explains the offset semantics with examples. It does not mention error handling or data availability edge cases, but for a simple weather lookup, the provided details are sufficient and not misleading.

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 compact and well-structured into paragraphs for purpose, arguments, return value, and examples. It front-loads the primary purpose and includes only relevant details. Each sentence adds value (constraints, examples) without redundancy. The formatting is clean and easy to parse.

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 low complexity and the presence of an output schema, the description covers all necessary aspects: both parameters with constraints, the return value, and illustrative examples. The description is complete for an agent to call the tool correctly. No critical information (e.g., units, timezone) 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 provides only types (string, integer) with no descriptions, and schema_description_coverage is 0%. The description fully compensates by explaining that location is a place name, lead_time_swiss is an hour offset from 00:00 Swiss local time, and it specifies the valid range (2–121). The examples clarify the time interpretation, adding substantial meaning beyond 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 clearly states 'Get total cloud cover percentage for a location at a specific offset time', specifying the exact resource (cloud cover percentage) and the parameters that distinguish it from siblings (location, specific offset time). The name matches the purpose, and the description includes the verb 'Get' and concrete examples that remove any ambiguity.

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 clearly indicates the tool is for retrieving cloud cover, but it does not explicitly compare to siblings like total_rainfall or temperature. However, the purpose is so specific that an agent can infer when to use it. The examples also illustrate valid call patterns, effectively showing when the tool applies, though no exclusions or alternative tools are named.

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 behavioral disclosure burden and does a reasonably good job: it states valid offset bounds (minimum 2 hours, maximum 121 hours), clarifies the timezone basis ('Swiss local time'), and specifies the return type and unit (float in millimeters). It does not discuss error behavior or data availability, which 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?

The description is well-structured with clear Args, Returns, and Examples sections, and the core purpose is front-loaded. The six examples are somewhat repetitive but collectively illustrate the meaning of the offset parameters effectively.

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?

This is a simple three-parameter tool with no nested objects or enum constraints, and the description covers all parameters, valid ranges, return units, and illustrative time-window examples. An agent has enough information to call the tool correctly without additional context.

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 0%, so the description must supply all semantic meaning. It explains 'location' with an example, and defines both lead_time_start_swiss and lead_time_end_swiss as hour offsets from today at 00:00 Swiss local time with min/max constraints. The examples further clarify the expected parameter relationships.

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 action ('Get total rainfall'), the resource (a location and offset period), and the return unit (millimeters). It is easily understandable and distinct enough from siblings like precipitation_rate, though it does not explicitly call out a sibling for differentiation.

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 examples imply usage scenarios such as 'total rainfall today' and 'tomorrow morning', which help an agent understand the intended time-window semantics. However, there is no explicit guidance on when to prefer this tool over alternatives like precipitation_rate or total_cloud_cover.

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 itself discloses that the value is predicted, constrains lead_time_swiss to 2–121 hours, and specifies output units in meters per second. It does not mention error behavior or data update characteristics, but for a simple read-only query it provides meaningful behavioral context.

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 docstring is organized with Args, Returns, and Examples, front-loading the tool's purpose. Each section adds useful information with no filler, and the examples clarify the offset-to-clock-time mapping efficiently.

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?

The tool has only two parameters, both thoroughly explained, and the return type and units are clearly described. It could additionally state what happens for invalid locations or out-of-range offsets, but those details are not required to call 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?

The schema provides only names and types with 0% description coverage, so the description must carry the load. It explains location as a location name and lead_time_swiss as an hour offset from 00:00 Swiss local time, with min/max bounds and worked examples, fully specifying parameter semantics.

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 opens with a specific verb and resource: 'Get predicted wind speed for a location at a specific offset time.' This clearly distinguishes it from sibling tools that return other weather variables such as temperature, total_cloud_cover, or precipitation_rate.

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 conveys usage context through the offset-time semantics and examples, but it never explicitly states when to choose this tool over siblings or gives exclusion criteria. Usage is implied rather than directly instructed.

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