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

Annotations already cover read-only and non-destructive behavior. The description adds useful context about the output content (quality score and improvement tasks) but does not disclose return format, scoring scale, or any limitations. This is adequate but not rich.

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 sentences: the first states what the tool returns, the second states when to use it. No wasted words and the important output purpose is front-loaded.

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?

For a read-only diagnostics tool with optional parameters, the description covers the core purpose and usage context. Gaps remain around the exact return shape and the meaning/effect of 'limite', but these are minor for an agent deciding to call it.

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?

The schema documents 'cuenta' well, but 'limite' has no semantic description. The tool description adds nothing about either parameter. With only 50% schema description coverage, the description should compensate for the undocumented 'limite' parameter and does not, leaving its meaning ambiguous.

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 identifies the tool's purpose: providing a MercadoLibre quality score per publication and concrete tasks to improve it. It names specific quality factors (photos, technical sheet, description, warranty), which distinguishes it from siblings like ml_rendimiento_publicaciones or ml_metricas.

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 gives an explicit trigger: use when users ask how to improve positioning or why a listing isn't selling. It does not mention when not to use it or name alternatives, but the stated context is clear enough for selection.

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