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 of behavioral disclosure. It adequately discloses return values (scores, suggestions, improved version) but fails to explicitly state whether this is a read-only analysis operation or if it modifies stored state. Given the 'improve' naming, explicit safety clarification would be valuable.

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 appropriately front-loaded with the core action and efficiently structured in two sentences. It loses one point for the cryptic '[query]' tag at the end, which appears to be template residue or unclear metadata that doesn't earn its place.

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 low complexity (single string parameter, no output schema), the description is complete. It compensates for the missing output schema by explicitly detailing what the tool returns: category scores, improvement suggestions, and an improved prompt version.

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 its single parameter, establishing the baseline. The main description does not add parameter-specific semantics beyond the schema, but it doesn't need to. The trailing '[query]' appears to be noise or a placeholder that doesn't add value.

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 ('Analiza', 'devuelve') and clearly identifies the resource (prompt). It explicitly distinguishes this from sibling tools like get_prompt or save_prompt by emphasizing AI analysis, scoring, and suggestions rather than simple CRUD operations.

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 clear when-to-use guidance ('Util cuando el usuario quiere saber si su prompt es bueno o como mejorarlo'). While it doesn't explicitly name prerequisite steps (like using get_prompt first), it successfully establishes the distinct use case for prompt optimization versus other prompt management operations.

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