WAzion MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It includes '[mutation]' indicating a write operation and clarifies scope (customer-facing WhatsApp AI), but lacks details on required permissions, reversibility, or immediate side effects on the AI system beyond recording the correction.

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?

Every sentence earns its place: opening establishes scope, middle provides usage constraints, and end clarifies sibling distinction. The front-loaded structure prioritizes the action ('Record Customer AI Correction') and target system before detailing edge cases. No redundant or filler text.

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 complexity of distinguishing content corrections from behavioral rules (a subtle domain distinction), the description provides complete context for correct agent selection. With 100% schema coverage and no output schema required for a simple record action, the description successfully navigates the critical sibling relationship with set_ai_error.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by providing concrete examples of content errors ('wrong info, wrong tone, unnecessary questions') that help agents understand what constitutes valid input for the fact_content, original_text, and category parameters.

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 correction for the CUSTOMER-FACING WhatsApp AI' with a specific verb and resource. It clearly distinguishes from the sibling tool set_ai_error by specifying this is for 'CONTENT of a response' rather than 'behavioral rules about when/whether to respond.'

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?

Provides explicit positive guidance ('Use ONLY when the CONTENT of a response sent to a customer was wrong'), negative constraints ('Do NOT use this for behavioral rules'), and explicitly names the alternative tool ('those belong in set_ai_error'). The IMPORTANT note further clarifies the boundary between content and behavioral corrections.

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