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 of behavioral disclosure. While it mentions filtering by snippet status, it lacks details on pagination, potential rate limits, auth requirements, or what the returned metrics represent (e.g., counts, accuracy scores, confidence levels).

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 reasonably brief at three sentences, but contains the artifact '[query]' at the end which appears to be a template placeholder or formatting error that wasn't removed, slightly degrading the quality.

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 lack of output schema and annotations, the description should explain what metrics are returned (e.g., snippet counts by status, learning accuracy). It also fails to clarify the relationship with 'get_autolearning_stats', leaving the agent without sufficient context to use the tool effectively.

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 has 100% coverage with clear enum values. The description adds valuable semantic context by specifying that the filter applies to 'snippets' ('estado de los snippets'), clarifying that these metrics relate to knowledge snippets rather than just abstract states, which supplements the schema nicely.

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 states the tool displays autolearning metrics ('Muestra las metricas del sistema de aprendizaje automatico') and mentions filtering capability. However, it fails to distinguish from the sibling tool 'get_autolearning_stats', which appears to serve a very similar purpose, creating ambiguity for the agent when selecting tools.

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 like 'get_autolearning_stats' or 'apply_training_suggestion'. There are no prerequisites, exclusions, or workflow context mentioned.

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