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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description correctly aligns with a non-destructive read operation. It adds value by specifying the return format: drought severity by area in square miles at each D0–D4 level per week. This is context beyond the annotations and helps the agent understand the data granularity.

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 three sentences long, front-loaded with the core purpose, followed by concrete examples and a brief note on the output. Every sentence adds value: the first states what it does, the second explains how to specify the area of interest, and the third describes the return data and use cases. No wasted 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?

Given the tool's moderate complexity and the presence of an output schema (which is noted as existing), the description covers the key aspects: how to specify the area, the date range constraints (implied via schema), and what the output contains. It does not explicitly mention the optional statistics_type parameter, but that is covered in the schema. Overall, it is sufficiently complete for an agent to call the tool correctly.

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 100% and each parameter is fully described in the schema, including examples for aoi and explanations for start/end dates and statistics_type. The description repeats some of this information (e.g., FIPS codes and state abbreviations) but does not add new meaning beyond what the schema provides. It provides useful context about the output but does not deepen parameter understanding.

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 retrieves weekly county-level drought statistics from the USDA/NOAA US Drought Monitor for a specific county or all counties in a state. It names the exact resource and distinguishes it from national-level tools. The verb 'Retrieve' and resource 'county-level drought statistics' are precise and unambiguous.

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 explains what the tool does and mentions it is useful for agricultural impact assessment, insurance risk modeling, and local drought planning, but it does not explicitly state when to use it instead of sibling tools like drought.county.weeks or drought.national.stats. There is no exclusionary guidance or alternative comparison, so usage context 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.