WAzion MCP Server

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

No annotations are provided, so the description carries the full burden. It includes '[mutation]' indicating a state change, but provides no details on what data the AI learns from, whether changes are immediate, if there are side effects on existing conversations, or the scope (global vs per-agent).

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 compact and front-loaded with the action. It is slightly redundant (repeating 'activate/deactivate' twice in different languages), but efficiently conveys the core purpose without extraneous information.

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 single-parameter toggle with no output schema, the description meets minimum requirements by identifying the feature being toggled. However, it lacks explanation of what 'auto-learning' entails functionally, which would help an agent understand if this is the correct tool for managing AI training behavior.

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 input schema has 100% description coverage ('true para activar, false para desactivar'), fully documenting the boolean parameter. The description adds no additional semantic context beyond the schema, but the schema is complete enough that additional description is unnecessary.

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 identifies the specific resource (auto-learning/aprendizaje automatico de la IA) and action (activate/deactivate) using both Spanish and English terminology. However, it fails to distinguish from sibling getter tools like `get_autolearning_metrics` or `get_autolearning_stats`, which also deal with the same feature but are read-only.

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?

No guidance is provided on when to use this tool versus alternatives. Given siblings like `apply_training_suggestion` and `dismiss_training_suggestion` also relate to AI learning behavior, the description should clarify this tool controls the global auto-learning toggle, not individual training decisions.

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