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

Annotations confirm readOnly, openWorld, idempotent, and non-destructive hints. The description adds significant behavioral context: it returns fresh monthly Cañuelas data, a national YTD anchor, and historical national slaughter data with specific time periods and sources, plus a structural bias warning. No contradictions 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 a single long sentence containing all necessary information, but it is dense and could be more readable with sentence breaks. Nonetheless, every clause adds value, so it is efficient rather than verbose.

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 no output schema, the description thoroughly explains the three returned components: fresh Cañuelas reading, national anchor, and historical context, with precise time periods and sources. It also includes a critical interpretation warning, making it fully self-contained for an agent.

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?

Input schema has zero parameters with 100% coverage. The description reinforces that no arguments are needed, which is already clear from the schema. For a zero-parameter tool, baseline is 4, and the description adds no extra syntax but confirms the empty-arg condition.

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 returns the 'Índice de Liquidación' (% females) as a leading indicator of liquidation vs. retention, sourced from Mercado Agroganadero. It distinguishes from siblings like get_indice_novillo by specifying the unique indicator and data source.

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 notes no arguments are required and provides context on what the tool returns. It warns that Cañuelas data runs structurally above national slaughter, recommending against direct comparison. However, it does not explicitly contrast with sibling tools or provide when-not-to-use scenarios.

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