US Drought Monitor

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

With no annotations provided, the description carries the transparency burden. It discloses the output format (percentage of area at each drought level), explains the severity categories, and clarifies the scope of data. However, it does not mention behavior on invalid inputs (e.g., both state and county_fips provided) or data freshness, which would be useful.

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 opening sentence, a helpful bullet list of drought levels, and concise parameter guidance. Every sentence adds value, and it is appropriately front-loaded.

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 quite complete for a two-parameter, read-only tool. It explains the return format and parameter usage. Missing is a note on what happens if both state and county_fips are supplied (precedence or error), but this is a minor gap given the overall clarity.

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 provides no descriptions (0% coverage), but the description fully compensates by explaining the state abbreviation format with examples, the five-digit FIPS code with an example, and the mutual exclusivity/optionality of the parameters.

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 current drought conditions from the US Drought Monitor.' It specifies the resource (US Drought Monitor) and differentiates from siblings by focusing on 'current' conditions, as opposed to history or summary 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 description provides clear instructions on when to use each parameter: state for statewide data, county_fips for county-level detail, and omitting both for national data. However, it does not explicitly compare to sibling tools or state when not to use this tool.

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?

Without annotations, the description carries the full burden. It transparently discloses the data source (US Drought Monitor), the update schedule (every Tuesday), and the return type (weekly severity percentages). It clearly implies a read-only operation. It does not mention limitations like data start date or pagination, but the presence of an output schema reduces the need to explain return structure.

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 concise and front-loaded. The first sentence states the core purpose, followed by what is returned and update cadence, then a structured Args list. Every sentence contributes useful information with no 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?

For a data retrieval tool with an output schema, the description is largely complete: it covers the data source, update frequency, and parameter formats. However, it does not explicitly note that parameters are optional or whether state/county_fips are mutually exclusive, which would enhance completeness. The output schema handles return values, so no gap there.

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 zero descriptions, so the description must compensate. It does so thoroughly with an Args section explaining each parameter (state, county_fips, start_date, end_date) including format and examples (e.g., 'CA', '06037', 'YYYY-MM-DD'). This fully adds 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 the tool's purpose with a specific verb and resource: 'Get historical drought conditions over time from the US Drought Monitor.' It further specifies that it returns weekly drought severity percentages, which distinguishes it from sibling tools like get_current_drought (current conditions) and get_drought_summary (summary data).

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 historical time-series data by emphasizing 'over time' and 'weekly' data points, but it does not explicitly mention when to use this tool versus the siblings or provide exclusion criteria. No alternatives are named, so guidance is implied rather than explicit.

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 explains what the tool returns: percentages for each drought level and the total affected area. However, it does not disclose data freshness, source, or potential limitations, leaving some behavioral aspects unspecified.

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 reasonably concise, with the main purpose stated upfront. The first and second sentences are slightly redundant (e.g., 'total affected area percentages' is repeated as 'total area affected'), but the overall structure is clear and not overly verbose.

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 (0 params, no nested objects, output schema present), the description covers the essential aspects: purpose, output content, and a usage hint. It does not explicitly explain how this tool compares to siblings, but the output schema handles return values, so the description is sufficiently complete for a low-complexity 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, and the schema coverage is complete (100%). The description adds context about the output (drought levels and total area), but with no parameters to describe, the schema and description together are sufficient. Baseline for 0 params is 4, and the description does not need to compensate for parameter 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 a specific verb and resource: 'Get a national drought summary with total affected area percentages.' It also specifies the scope ('across the entire country') and the content (drought levels D0 through D4), which distinguishes it from sibling tools like get_current_drought and get_drought_history.

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 a usage context: 'Useful for getting a quick picture of drought conditions across the entire country.' This implies when to use the tool, though it does not explicitly mention alternatives or when not to use it. The context is clear enough to differentiate from more detailed or historical tools.

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