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 disclosure burden. It compensates by listing return fields (score 0-10, categoria, intencion, consejo, urgencia) and warning about AI credit consumption. However, it fails to state whether the operation is read-only, destructive, or idempotent, and omits rate limits or error behaviors.

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 relatively concise at one sentence plus output/credit notes. However, the trailing '[query]' appears to be noise or a placeholder that earns no place, and the Spanish-only text amidst English sibling tools creates slight friction. Structure is front-loaded with the em-dash separator, which is effective.

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 8 parameters, no annotations, and no output schema, the description partially compensates by documenting the return structure (score, category, intention, advice, urgency). However, it lacks explanation of how the time-based parameters interact (days vs hours vs last_n) and omits guidance on the 'tone' parameter's effect.

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?

Schema description coverage is 100%, documenting all 8 parameters including the phone requirement and summary_type enum. The description adds no additional parameter semantics beyond the schema, relying entirely on structured documentation. With complete schema coverage, this meets the baseline expectation.

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 'analiza el sentimiento e intencion del cliente en una conversacion' (analyzes sentiment and intention in a conversation) using AI, providing specific verb and resource. However, it does not explicitly differentiate from sibling tool 'get_sentiment_trend', leaving ambiguity about whether this analyzes a single conversation or aggregates multiple.

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 only notes that it 'Consume creditos de IA' (consumes AI credits), warning about cost. It lacks explicit guidance on when to use this versus 'get_sentiment_trend' for bulk analysis, and does not mention prerequisites like requiring the phone parameter or how time filters (days/hours/last_n) interact.

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