Números del mes (contabilidad)

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

Annotations already declare readOnlyHint=true, and the description adds real behavioral value beyond that: it will flag the result as 'parcial' when costs or taxes are missing, names what is missing, and explicitly promises it never invents data. It also discloses that tax percentages and costs come from 'el cerebro' (ml_cerebro), which matters for interpreting the output.

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?

Purpose and output shape are front-loaded before the parameter note, and every clause about missing costs/taxes earns its place. The 'Carmen, la contadora' framing is a small flavor cost and the single sentence is long, but nothing is truly redundant.

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 the return: it enumerates the components, the computed net result and margin, the previous-month comparison, and the partial-result flag. An agent knows what it will get back; only the multi-account ('cuenta') case is left to the schema.

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%, so the schema already documents both parameters. The description restates the 'mes' formats but usefully adds that 'actual' is the default and means 'hasta hoy'; the 'cuenta' parameter is not mentioned in the description at all beyond the schema's pointer to ml_cuentas. That lands at the baseline.

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 (monthly accounting summary: billing, sales count, real ML commissions, ads, taxes, COGS, shipping → net result and margin) and states the comparison scope. That is far more than a restatement of the name, and it implicitly sits above the more granular siblings (ml_comisiones, ml_publicidad, ml_envios), though it never explicitly contrasts itself with them.

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?

It explains the 'mes' argument values including the default, which implies when the tool is relevant, but never states when to call this versus ml_rentabilidad, ml_tablero or ml_panel_ventas. Usage is inferable from the data list, not spelled out.

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