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

The annotations already declare readOnlyHint and openWorldHint, but the description adds valuable behavioral context beyond that: it notes a $0.05 per-call cost, that values are as published including NASS suppression markers, and that the tool is experimental with potential changes or withdrawal. These are meaningful operational traits that help an agent budget and trust the data, going beyond the structured annotations.

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 well-structured, front-loading the important 'Experimental' warning before the core purpose and cost. Every sentence adds value: the experimental status, the data source and types, the data fidelity note, and the cost. It's slightly long but not bloated, and the key information is prioritized.

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 5 parameters and no output schema, the description covers the data source, statistic types, geographic scope, and cost—good foundation. However, it does not describe the return format (e.g., JSON, CSV) or pagination behavior, which an agent would need to parse results. The mention of suppression markers hints at data handling but not the response structure. This is a moderate gap given no output schema exists.

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 100%, so the schema already documents all five parameters with appropriate details. The description adds little new per-parameter meaning; it reinforces that statistics cover yields, production, etc., which mirrors the 'statistic' enum. Since the schema carries the semantic weight, a baseline of 3 is appropriate—the description does not compensate for any 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 it provides official USDA NASS estimates, listing the specific statistic types (yields, production, area, stocks, prices) and geographic scope (national or by state). This is a specific resource and purpose, distinguishing it from siblings like energy_data or fx_rate. It could be even more explicit about the tool's name, but the purpose is 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 offers no guidance on when to use this tool versus alternatives. It only warns that it's experimental and may change, which is a caveat but not usage direction. There is no mention of when to prefer crop_data over other data tools like energy_data or trade_flows, nor any exclusions or prerequisites.

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