local_status

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

Annotations already set readOnlyHint=true, and the description reinforces read-only nature with 'solo lectura'. It adds context about checking backend liveness and model catalog availability, which goes beyond the annotation's binary hint. No negative behaviors omitted.

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 concise, front-loaded sentences. No wasted words. Every sentence adds value: first states what it is, second tells when to use it.

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 zero parameters, an output schema present, and no nested objects, the description fully covers purpose and usage. It is complete for a diagnostic tool.

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?

No parameters exist (empty schema, 0 params, coverage 100%). Baseline is 4 per rubric. Description does not need to add param info.

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 'Diagnóstico de solo lectura del backend local y el catálogo de modelos' and explicitly distinguishes its diagnostic role from sibling local_* tools by mentioning it diagnoses why a local_* tool failed. The verb 'diagnóstico' and resource 'backend local y catálogo de modelos' are specific.

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?

Explicitly tells when to use: 'para saber qué modelos locales hay disponibles y verificar que el backend está vivo antes de delegar en masa, o para diagnosticar por qué una tool local_* falló'. This gives clear context and exclusions (use before delegation or after failure).

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