flood

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions what data is returned, it doesn't describe important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or data freshness. For a forecasting tool with no annotation coverage, this 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 a single, efficient sentence that clearly states the tool's purpose. It's appropriately sized for a simple data retrieval tool and front-loads the key information. There's no wasted verbiage, though it could potentially be more structured for complex tools.

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 forecasting tool with 3 parameters and no output schema, the description provides basic purpose but lacks important context. It doesn't explain what format the forecast data returns, temporal resolution, confidence intervals, or how to interpret the discharge values. With no annotations and no output schema, the description should do more to help the agent understand what to expect.

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 has 100% description coverage, providing clear documentation for all three parameters. The description adds no parameter-specific information beyond what's in the schema. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding but doesn't need to compensate for gaps.

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: 'Get a comprehensive flood forecast including river discharge, mean discharge, and max discharge for a location.' It specifies the verb ('Get'), resource ('flood forecast'), and key data elements. However, it doesn't explicitly differentiate from the sibling tool 'get_river_discharge' beyond mentioning 'comprehensive' flood 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 provides no guidance on when to use this tool versus the sibling 'get_river_discharge'. It doesn't mention any prerequisites, alternatives, or exclusions. The agent must infer usage from the tool name and description alone without explicit 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 provided, the description carries the full burden of behavioral disclosure. It specifies the data source (Open-Meteo Flood API) and units (m³/s), which adds useful context beyond what the input schema provides. However, it doesn't describe important behavioral aspects like rate limits, authentication requirements, data freshness, or what the response format looks like.

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, efficient sentence that communicates the essential information without any wasted words. It's appropriately sized for this type of data retrieval tool and front-loads the key information (what it gets, what units, what API).

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 data retrieval tool with 3 parameters, 100% schema coverage, but no output schema and no annotations, the description is adequate but has clear gaps. It explains what data is retrieved but doesn't describe the response format, which is important since there's no output schema. The mention of the specific API source adds useful 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?

The schema description coverage is 100%, so all parameters are well-documented in the schema. The description doesn't add any additional parameter semantics beyond what's already in the schema descriptions. It mentions 'geographic location' which aligns with the latitude/longitude parameters, but provides no new information about parameter usage or constraints.

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 daily river discharge forecast'), resource ('river discharge forecast'), units ('m³/s'), and data source ('Open-Meteo Flood API'). It distinguishes from the sibling tool 'get_flood_forecast' by specifying it's for discharge data rather than general flood forecasting.

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 about when to use this tool (for river discharge forecasts at geographic locations), but doesn't explicitly state when NOT to use it or provide specific alternatives. The existence of a sibling tool 'get_flood_forecast' suggests there are related alternatives, but the description doesn't explain the distinction between them.

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