USGS Water Monitoring

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

Since no annotations are provided, the description carries the full burden. It discloses that it checks 'the most recent instantaneous values against known flood stages', revealing the freshness and method. It doesn't mention side effects or permissions, but as a read-only query this is acceptable; it adds value 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?

The description is only a few sentences, with the purpose first, followed by return behavior and parameter spec. Every sentence earns its place; no redundant or vague wording.

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 tool (1 param, no nested objects) and the presence of an output schema, the description covers all necessary contextual aspects: purpose, behavior, and parameter details. It is complete and self-sufficient.

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%, but the description compensates fully with 'state: Two-letter US state abbreviation (e.g. 'CA', 'TX', 'LA')'. This adds necessary formatting and examples, making the parameter semantics excellent.

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 starts with 'Get current flood conditions for USGS monitoring sites in a state', which is a specific verb+resource+scope. It distinguishes itself from siblings by focusing on flood stage exceedance, explicitly indicating 'sites where water levels are above flood stage'.

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 implies this tool is for flood-specific queries ('active flooding or near-flood conditions'), giving strong contextual guidance. It doesn't explicitly mention alternatives or exclusions, but the context makes the intended use clear relative to siblings like get_water_levels.

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 of behavioral disclosure. It clearly states the operation is a read-only search ("Find") and specifies the return content: a list of monitoring stations with site numbers, names, and locations. It also notes the default limit. However, it doesn't explicitly mention error behavior, pagination, or rate limits, but these are less critical for a simple lookup tool and the output schema covers return format.

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 and concise: a clear purpose sentence, a sentence about the return value, a pointer to the next tool, and a neatly formatted Args section. No wasted words, every sentence 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?

Given the moderate complexity (three parameters, no nested objects) and the presence of an output schema, the description is complete. It covers purpose, usage, parameter semantics, and the relationship to sibling tools. The only minor omission is no explicit 'when not to use,' but the clear separation of tool purposes makes this unnecessary.

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%, but the description fully compensates by explaining each parameter: state is a two-letter abbreviation with examples, site_type lists the meaning of each code, and limit includes a default. This adds significant meaning beyond the bare schema fields.

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 a specific verb and resource: 'Find USGS water monitoring sites in a state.' It distinguishes itself from siblings (get_flood_status, get_water_levels) by focusing on site discovery, and explicitly mentions the connection to get_water_levels.

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 explicit usage guidance: 'Use site numbers with get_water_levels to retrieve data.' This directly tells the agent when and how to use this tool in a workflow, referencing a sibling tool. It also explains the meaning of site_type with examples, clarifying when each option is appropriate.

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 does state that the tool returns time-series data and notes the max days constraint, but it doesn't mention potential errors, data availability issues, or any other behavioral traits beyond the basic read operation.

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 tightly written: a two-sentence overview followed by a structured Args list. Every sentence contributes meaningful information without any fluff 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?

Given the tool's simplicity (2 params, output schema present), the description is complete. It covers purpose and parameters thoroughly; return value details are delegated to the output schema, which is appropriate.

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%, but the description's Args section fully compensates by explaining both parameters. It provides a concrete example for site_number and specifies the default and maximum for days, adding meaning well 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 first sentence clearly states the tool retrieves recent water level and streamflow data for a USGS monitoring site. It is specific with a verb+resource and even includes an example site number, distinguishing it from sibling tools like get_flood_status and get_sites_by_state.

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 retrieving time-series data from a specific site but does not explicitly state when to use this tool over alternatives or provide exclusions. For instance, it doesn't mention that get_flood_status should be used for flood-specific queries or get_sites_by_state for site discovery.

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