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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds meaningful context beyond this: it tells the agent that prices are 'mandatory-reporting', includes 'preliminary and final weekly prices' used in FMMO formulas, and cites the USDA AMS as source. This informs the agent about data authority and temporal granularity. No contradiction with 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 dense but efficient. It leads with the product name and official code, then lists the exact products, mentions the FMMO relevance, and ends with source attribution. Slight redundancy (e.g., 'weekly' twice, 'USDA' twice) but no wasted sentences.

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 presence of an output schema and two well-documented optional parameters, the description supplies the key contextual elements: data type, products, source, frequency, and public-domain status. It does not need to explain return structure since the output schema exists. Missing nuances like exact formatting or revisions are covered by schema or are non-essential for invocation.

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?

Both parameters are fully described in the input schema (report_date format and all_sections behavior), so the schema carries the burden. The description does not add any parameter-specific guidance beyond what the schema already 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 clearly states the resource: 'mandatory-reporting prices and volumes' for specific dairy products (butter, cheddar block/barrel, dry whey, nonfat dry milk). This immediately distinguishes it from sibling livestock price tools like lmpr.beef.boxed_cutout or lmpr.cattle.slaughter_prices. The inclusion of the official code DYWDAIRYPRODUCTSSALES adds unambiguous identification.

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?

Usage is implied through the product list and the weekly cadence, but there is no explicit statement of when to use this tool instead of alternatives. It does not reference sibling lmpr.* tools or mention that it is not for cattle/hog/lamb prices. The description relies on domain specificity rather than direct routing.

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