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 includes '[mutation]' tag indicating state modification, and clarifies scope (records mistakes in 'this dashboard chat' vs external customer content). It could improve by explicitly stating the record is persistent or retrievable via get_ai_errors, but adequately covers the immediate behavioral impact.

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?

Front-loaded with action verb 'Record', followed immediately by scope clarification. The 'IMPORTANT' callout efficiently distinguishes from set_customer_ai_error. Every sentence serves a distinct purpose: definition, trigger conditions, content scope, and sibling differentiation. No redundant or wasted text despite covering complex distinctions.

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 moderate complexity (3 params, 100% schema coverage, no output schema), the description is complete. It addresses the critical business logic distinction between self-correction and customer-error reporting, explains the mutation nature, and provides sufficient context for an agent to confidently select between this and the sibling set_customer_ai_error tool.

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?

While schema coverage is 100% (baseline 3), the description adds semantic context by mapping parameters to concepts: 'wrong answers' maps to original_text, 'behavioral rules' maps to fact_content, and the category examples align with the 'shipping, pricing, warranty...' enum. This adds meaningful interpretation of what constitutes valid parameter content beyond raw schema definitions.

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 explicitly states the tool 'Record[s] a mistake in YOUR OWN responses or behavior' with specific scope (dashboard chat). It clearly distinguishes from sibling tool set_customer_ai_error by specifying this is for 'YOUR errors' versus 'errors in the CONTENT of responses sent to customers by the WhatsApp AI', providing precise sibling differentiation.

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?

Explicitly states when to call: 'when the user corrects you, AND when you proactively detect you did something wrong'. It provides clear alternative guidance: 'For errors in the CONTENT of responses sent to customers by the WhatsApp AI, use set_customer_ai_error instead', covering both when-to-use and when-not-to-use scenarios.

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