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 safety profile is covered. The description adds valuable behavioral context: it states the tool combines 'latest/as-of values, prior-year changes, and exact five-year comparisons', covers 'crop years 2019–2025', and explicitly notes it is 'not a current forecast or trading recommendation'. This goes beyond the annotations and helps set expectations about the data vintage and scope.

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, information-dense paragraph that front-loads the core purpose and then adds usage examples and scope constraints. It's slightly long but every sentence earns its place: purpose, examples, scope, and a disclaimer. The structure is clear and readable.

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 read-only evidence-pack tool with 100% schema coverage and no output schema, the description covers the key contextual needs: what data is included, what time range is covered, and what the tool is not (not a forecast/trading recommendation). It doesn't describe the exact output structure, but since there's no output schema and the tool is an evidence pack, a bit more detail on the return format could help. However, the description is largely complete for an agent to decide whether to call it.

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 four parameters (crop, as_of_year, crop_class, response_format). The description adds context about the crop-year coverage (2019–2025) and the evidence-pack output concept, but it doesn't add much parameter-level detail beyond what the schema provides. Baseline 3 is appropriate.

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 uses a specific verb ('Combine') and resource ('hosted final annual USDA NASS national metrics for one major crop/class') and clearly distinguishes this evidence-pack tool from siblings like usda_nass_crop_history, usda_nass_crop_trend, and usda_nass_compare_crop_years. It also gives concrete example queries ('build a corn production evidence pack', 'summarize national soybean acreage yield and output').

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 explicitly states when to use this tool ('Use this for...') and provides example use cases. It also names an alternative context ('as an agriculture leg to combine with weather, freight, fuel, or futures data'), which helps an agent understand when this tool is the right choice versus other data tools. It doesn't explicitly name sibling alternatives, but the evidence-pack framing and examples make the usage context clear.

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