Weather

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

No annotations are provided, so the description carries the full burden. It mentions the return data types but lacks critical behavioral details such as data sources, update frequency, rate limits, error handling, or authentication requirements. For a tool with no annotations, this is a significant gap in transparency.

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 appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and return values without unnecessary details. Every sentence earns its place by providing essential information 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?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It covers the basic purpose and output but lacks details on behavioral aspects like data freshness or limitations. Without annotations or output schema, more context on return format or errors would improve completeness.

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 input schema fully documents all parameters (latitude, longitude, days). The description adds no additional parameter semantics beyond what the schema provides, such as format details or usage examples. Baseline score of 3 is appropriate as the schema handles the heavy lifting.

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 purpose with specific verbs ('Get') and resources ('multi-day weather forecast for a location'), and distinguishes it from the sibling tool 'get_weather' by specifying it returns forecast data rather than current conditions. It explicitly lists what information is returned (daily high/low temperatures, precipitation, and conditions).

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 context by specifying it's for 'multi-day weather forecast,' suggesting when to use it versus alternatives like current weather tools. However, it doesn't explicitly state when not to use it or name the sibling tool 'get_weather' as an alternative, leaving some ambiguity about tool selection.

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 mentions the return data structure (temperature, humidity, wind speed, conditions) which is helpful, but doesn't address important behavioral aspects like rate limits, error conditions, authentication requirements, or data freshness. The description adds some value but leaves significant gaps.

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 perfectly concise - two sentences that efficiently communicate the tool's purpose and return values without any wasted words. It's front-loaded with the core functionality and follows with essential output information, making it easy for an agent to quickly understand what the tool does.

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 read-only weather tool with no annotations and no output schema, the description provides basic functionality and return format information. However, it lacks important context about error handling, data sources, units of measurement, or temporal aspects of 'current' weather. Given the simplicity of the tool, the description is adequate but could be more 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?

The schema description coverage is 100%, with both latitude and longitude parameters fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline expectation but doesn't provide additional semantic context about coordinate formats, valid ranges, or special cases.

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 specific action ('Get current weather conditions') and resource ('for a location'), distinguishing it from the sibling tool 'get_forecast' which likely provides future predictions rather than current conditions. It uses precise language that leaves no ambiguity about the tool's function.

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 for obtaining current weather data, but doesn't explicitly state when to use this tool versus the 'get_forecast' sibling. There's no guidance about alternative scenarios or exclusions, leaving the agent to infer the distinction based on the 'current' versus 'forecast' terminology.

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