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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, establishing the tool's non-destructive, read-only nature. The description adds valuable context beyond annotations: the publication cadence ('Published daily on USDA business days'), data freshness ('prior-day'), and licensing/public-domain status ('US Government public domain'). It does not contradict annotations and enriches the behavioral profile without redundancy.

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 tightly written in three sentences: it states the core purpose first, then enumerates return fields, and closes with publication/source context. Every sentence adds value, with no fluff. It front-loads the most essential information (what and where) and keeps additional detail succinct.

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 tool with two optional parameters, an existing output schema, and comprehensive annotations, the description covers the essential context: exact data fields, market coverage, publication frequency, and source. It does not explain the output format, but the output schema handles that. It might benefit from noting any rate limits or usage constraints, but these are not critical given the tool's simple nature. Overall, it is well-rounded and sufficient.

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 documents both parameters (report_date and all_sections) with descriptions, achieving 100% schema coverage. The tool description does not add any parameter-specific details beyond what the schema provides. Since the schema already carries the full semantic weight for parameters, the description's lack of parameter discussion is acceptable and matches the baseline of 3.

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 primary function: retrieving national daily prior-day hog slaughter prices (LM_HG201). It enumerates the specific data fields returned (barrows/gilts head counts, base prices, carcass weight ranges, net price distribution, and 14-day commitments) and identifies the geographic scope (Corn Belt and national markets). This distinguishes it from sibling tools like lmpr.beef.boxed_cutout or lmpr.cattle.slaughter_prices by its unambiguous focus on hogs.

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 context: it returns hog-specific price data, covers specific markets, and is published daily on USDA business days. However, it does not explicitly state when to use this tool over alternatives (e.g., when to choose it over lmpr.cattle.slaughter_prices) or any exclusions. The clarity of the resource type implicitly guides selection, but explicit comparative guidance is missing.

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