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

Annotations already cover the read-only, idempotent, non-destructive profile, so the bar is lower. The description still adds real value by disclosing the return payload (fresh Cañuelas monthly reading 2026→, YTD national anchor from MAGyP PDFs, historical national female-slaughter series 1998-2025) and the comparability caveat.

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?

Front-loads the core definition before the data-source and caveat detail. Dense with parentheticals and em-dashes, but each clause (sources, periods, caveat, EN gloss) earns its place; only the bilingual gloss feels optional.

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?

With no output schema, the description carries the burden and does so: it names every returned component with its source and periodicity, plus the cross-series comparability warning. An agent can call and interpret it without further context.

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?

Zero parameters, so the baseline of 4 applies. 'Sin args' explicitly confirms no inputs are needed, matching the empty schema.

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?

States a specific metric — the share of females (cows + heifers) in cattle moved through the Cañuelas market — and frames it as a leading indicator of liquidation vs. retention. This is precise enough to distinguish it from sibling data tools like cattle_price_index or inmag_history.

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?

Gives interpretive context (leading indicator of liquidation vs. rebuilding) and an important caution not to compare Cañuelas levels 1:1 with the national slaughter figure. However, it never states when to pick this tool over siblings like price_dispersion_history or macro_context; usage is implied rather than routed.

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