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review_automatica

Review automatic rules inactive for N days and demote stale ones to semantic level, preserving useful data. Use dry-run to preview candidates before applying.

Instructions

Revisa reglas 'automatica' sin revisión en los últimos N días.

Las reglas automatica son permanentes por diseño, pero pueden degradarse a 'semantica' si no han sido útiles (review_count=0) después de max_age_days.

Con dry_run=True solo lista las reglas candidatas a degradación. Con dry_run=False las degrada a 'semantica' (no se borran, solo pierden inmunidad de compresión).

Args: max_age_days: Edad máxima sin revisión antes de considerar degradación (default 30). dry_run: Si True, solo muestra qué se degradaría.

Returns: Reporte de reglas encontradas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNo
max_age_daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It explicitly discloses that dry_run=False degrades rules to 'semantica', that rules are not deleted, and that they lose compression immunity. This covers the main side-effects and mutation behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with an intro, explanation, args, and returns. Each sentence serves a purpose; no fluff. The dry-run behavior is repeated in prose and args for clarity, but not excessively.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the rule lifecycle (permanent vs degradable), the condition for degradation (review_count=0 after max_age_days), and the effect of degradation. Since an output schema exists, return values are covered. It is complete for this tool's scope.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It clearly explains both parameters: 'max_age_days' as age threshold and 'dry_run' as toggle between listing and actually degrading. This adds meaningful semantics beyond the schema's titles and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Revisa reglas automatica') and the specific resource ('reglas automatica sin revisión'), and explains the additional degradation behavior. It distinguishes from siblings by focusing on automatica rules and their lifecycle.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Clear context is provided: use this to review automatica rules not reviewed in N days and potentially degrade them if review_count=0. No explicit alternatives or exclusions are mentioned, but the trigger conditions are well-explained.

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

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