WAzion MCP Server

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

With no annotations provided, the description carries the full burden. It partially discloses what data is returned (snippet counts by status), but lacks information about side effects, caching behavior, or performance characteristics expected for a statistics retrieval tool.

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 efficiently structured with a clear Spanish title prefix and colon-delimited details. It is front-loaded with the core purpose. The '[query]' suffix appears to be template residue but minimally impacts clarity.

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 zero-parameter tool without output schema, the description provides adequate context by listing example statistic categories. However, given the existence of the highly similar sibling 'get_autolearning_metrics', the description should clarify the distinction between 'stats' and 'metrics' to be complete.

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 contains zero parameters (empty object), which establishes a baseline of 4. The description does not add parameter semantics, but the trailing '[query]' appears to be an artifact rather than functional documentation.

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 obtains detailed statistics from the auto-learning system and lists specific categories (active snippets, pending, rejected). However, it does not explicitly differentiate from the sibling tool 'get_autolearning_metrics', which has very similar naming.

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 (particularly 'get_autolearning_metrics'), nor does it mention prerequisites, rate limits, or exclusion criteria.

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