Historial de competencia

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

readOnlyHint=true already establishes the read-only safety profile. Beyond that, the description adds genuinely useful behavioral context: the store is automatically populated by each ml_novedades_competencia check, so the agent knows it is a passively-maintained derived dataset rather than a user-written one, and knows its freshness depends on those checks.

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?

Two sentences that front-load the core identity ('La base de datos de competencia') before the enumeration and the provenance note. No filler, though the three-variant list is slightly dense and the trailing sentence is a bit of an aside.

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 of describing what comes back, and it does list the tracked metrics and the time-evolution nature, so the agent can anticipate the payload. It does not specify granularity, date-range handling, or pagination, but for a read-only tool with a rich annotation and full schema coverage it is largely complete.

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% and the single 'ref' parameter is fully documented there (including the item_id/nickname/keyword forms). The description repeats the notion of a tracked objective but adds no syntax, format, or matching rules beyond the schema, so the 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 names a specific resource — a competition history database tracking the evolution over time of a watched objective — and enumerates three concrete variants (rival publication price/sales, search leader price, seller publication count/median price). It also names the sibling ml_novedades_competencia as its data source, which helps distinguishing it from that tool. It lacks an explicit action verb ('consultar'/'obtener'), but the noun-phrase scope is nonetheless unambiguous.

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 note that the database 'se alimenta sola con cada control de ml_novedades_competencia' implicitly tells the agent that ml_novedades_competencia drives updates while this tool serves the historical view. However, there is no explicit 'use this when / do not use this when' statement and no comparison against siblings like ml_tendencias or ml_descubrir_ganadores, leaving selection partly to inference.

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