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 successfully discloses that results include token counts and costs, providing insight into the data structure. However, it omits pagination behavior details, rate limiting, or whether results are cached/real-time, which would be expected for a reporting tool with no annotation coverage.

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 the purpose front-loaded ('Ver detalle de uso por consulta') followed by specifics. It avoids redundancy, though the trailing '[query]' tag is slightly ambiguous and doesn't add clear value.

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 straightforward purpose (retrieving granular usage logs) and simple parameter schema, the description adequately covers what the tool returns (individual AI queries with tokens/costs). While it could explicitly mention pagination support, the parameter names make this implicit.

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 for all 4 parameters (from, to, page, limit). The description does not add semantic meaning beyond what the schema already provides, meeting the baseline expectation for high-coverage schemas.

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 uses specific verbs ('Ver', 'Muestra') and clearly identifies the resource (individual AI queries with tokens and costs). It effectively distinguishes from sibling tools like 'get_usage_stats' by emphasizing 'detalle individual de cada consulta' (individual detail of each query) versus aggregate statistics.

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?

While the description implies this is for granular inspection ('detalle individual') as opposed to summary views, it lacks explicit guidance on when to use this tool versus siblings like 'get_usage_stats' or 'get_api_usage_stats'. No prerequisites or exclusions are mentioned.

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