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 compensates by disclosing what data categories are returned (performance, failures, credits, etc.), but fails to clarify read-only safety (implied by 'get' but not stated), pagination behavior, or whether data is real-time versus cached.

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 an em-dash separating the summary from the specific metric list. It avoids redundant verbosity. The trailing '[query]' appears to be metadata leakage that slightly detracts from the clarity, but overall it is appropriately sized and front-loaded.

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?

Without an output schema, the description partially compensates by listing the categories of statistics returned. However, it lacks details on the response structure (e.g., whether it returns time-series data, aggregates, or JSON objects), units of measurement, or any limitations on the date range queried.

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?

Input schema has 100% description coverage with clear Spanish descriptions of the date parameters and their defaults. The description does not add parameter-specific semantics, but with complete schema documentation, the baseline score of 3 is appropriate.

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 retrieves mass marketing statistics and specifies the exact metrics returned (campaign performance, failure breakdowns, hourly send distribution, list quality, credit balances). This distinguishes it from siblings like `get_campaign` (individual campaign details) and `get_analytics` (general analytics) by emphasizing aggregate statistical data.

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 explicit guidance on when to use this tool versus alternatives. While the specific metrics listed imply this is for reporting/overview purposes, there is no 'when to use' or 'use X instead for Y' guidance, forcing the agent to infer applicability solely from the name and metric list.

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