River Water Watch

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 adds useful context about data being 'official', 'cruise-critical', and including a 30-year percentile, but it does not explicitly confirm read-only behavior or describe any potential side effects. For a simple read operation, this is adequate 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, well-structured sentence that conveys purpose, scope, units, and output details without redundancy. Every phrase adds value.

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?

Since there is no output schema, the description adequately explains what the tool returns: readings in cm, station state, and 30-year percentile, across four rivers. It lacks explicit definitions of 'state' but is complete for a simple read-only gauge 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 tool has zero parameters, so the description is not required to explain them. The schema is trivially covered, and the baseline for zero-parameter tools is 4.

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 returns today's official water-level readings (cm) for specific rivers (Rhine, Danube, Elbe, Seine), including station state and percentile. This specific verb-resource pairing distinguishes it from siblings like get_monthly_odds and score_sailing.

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 phrase 'Today's' implies usage for current-day readings, but there is no explicit guidance on when to use this tool versus alternatives like get_monthly_odds or get_month_detail. No exclusions or alternative references are provided.

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 transparency burden. It discloses the categories of data returned (hydrology, 30-year frequency, last five years' outcomes), which is helpful, but it does not mention whether the operation is read-only, any side effects, limitations, or data freshness.

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, well-structured sentence that front-loads the purpose and itemizes the three output facets. There is no redundancy or filler, making it highly concise 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 simple 2-enum-parameter tool with no output schema and no annotations, the description adequately covers the main output types and enough context for an agent to invoke it correctly. It falls short only in not specifying output format or edge conditions, but the tool is straightforward enough that this is not a critical gap.

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 is the only source of parameter meaning. It does tie the parameters to the tool's purpose ('one of the four rivers', 'a given month'), but it adds no detail about how specific values affect the response. The enums in the schema already list valid values, so the description adds minimal semantic value beyond that.

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 explains what the tool provides: an analysis of why a given month behaves as it does on one of four rivers, including seasonal hydrology, 30-year frequency, and recent outcomes. It distinguishes itself from siblings like get_live_gauges and get_monthly_odds by focusing on explanatory detail rather than live readings or probabilities.

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 use when one needs explanatory context for a month on a river, but it does not explicitly state when to prefer this over sibling tools or mention any exclusions. The purpose is clear enough to infer usage, but no alternative guidance is provided.

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 burden. It clarifies the meaning of 'disruption odds' as chance of low water (and high water on the Seine), and notes the data is refreshed daily from official sources. It doesn't disclose return format or any side effects, but for a read-only tool the key behavior is defined.

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 sentences, front-loaded with the main output and immediately clarifying the risk definition. No filler words.

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 no output schema, so the description should specify result structure. It does not indicate whether the odds are returned per month, as a single aggregate, or in what format. The 12-month phrase is ambiguous between a single odds over the year and monthly odds, and 'monthly' in the name is not reinforced in the text.

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 has only a river enum with no descriptions, so the description is the sole source. It adds the important caveat that the Seine is specifically included for high-water risk, which affects interpretation of results. However, it doesn't explicitly enumerate the valid river choices, leaving the agent to rely on the schema's enum.

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?

Description names a specific resource ('cruise-disruption odds') and scopes it to a 12-month horizon and one of Europe's main cruise rivers. It distinguishes from sibling tools (gauges, month detail, sailing score) by focusing on odds rather than live readings or specific days.

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 context (12-month outlook, daily refresh) that implies use for planning, but it never explicitly says when to use this tool versus alternatives like get_live_gauges or score_sailing. No exclusions or alternative recommendations are provided.

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 provides meaningful methodology: it uses the live forecast to ~7 months, the 30-year weekly record beyond that, and actual same-date outcomes from the last five years. It also highlights the Seine's high-water inversion, adding useful context beyond the schema.

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 sentences, front-loaded with the core purpose and followed by a compact explanation of data sources. No filler or redundancy.

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 description is largely complete for a medium-complexity scoring tool with no output schema: it explains the purpose, the special Seine case, data horizons, and historical anchoring. Minor omissions are the 'nights' parameter and explicit output scale, but these do not undermine overall usability.

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 description adds meaning for river and departure_date via 'sailing departing on a given date' and 'river's tightest stretch,' but does not mention the optional 'nights' parameter. With 0% schema description coverage, leaving one of three inputs unexplained is a notable gap.

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 what the tool does: it scores a specific river cruise by estimating the chance of low water (or high water on the Seine) at a given date and river's tightest stretch. This distinguishes it from siblings like get_live_gauges and get_monthly_odds.

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 frames the tool as being for a specific sailing with a given departure date, establishing clear context for when to use it. It does not explicitly name alternative tools or state when not to use it, stopping 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.