get_autolearning_metrics

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

No annotations are present, so the description carries full responsibility for behavioral disclosure. It does not mention whether the operation is read-only, requires authentication, has rate limits, or any side effects. The description merely lists what is shown, without behavioral context.

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?

The description is a single sentence, which is concise, but it includes a trailing "[query]" that is unclear and may confuse the agent. The purpose is front-loaded, but the extra bracket adds noise. It could be more structured without sacrificing brevity.

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?

Given the tool's simplicity (no parameters, no output schema, no annotations), the description provides adequate detail about the content of the metrics. However, it does not explain the return format or any potential filtering, and it does not clarify the difference from sibling tool get_autolearning_stats. This leaves some gaps in completeness.

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 tool has zero parameters and schema coverage is 100% (empty object). The description adds value by specifying the types of metrics included (KPIs, distribution, evolution), which goes beyond the empty schema. Baseline for 0 parameters is 4, and the description meets that 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 clearly states it displays autolearning metrics including KPIs, distribution by category, and similarity evolution. It uses a specific verb ("Ver" / "Muestra") and resource, making the purpose clear. However, it does not differentiate from sibling tool get_autolearning_stats, which may have overlapping functionality.

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 description provides no guidance on when to use this tool versus alternatives. It lacks explicit context such as when the tool is appropriate, prerequisites, or exclusions. This leaves the agent to infer usage without support.

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