WAzion MCP Server

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

No annotations are provided, so the description must carry the full behavioral burden. It clarifies the scope is specifically 'CUSTOMER-FACING' WhatsApp AI (not internal/agent errors). However, it lacks disclosure on whether this is read-only, pagination behavior, or what the response structure looks like.

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 front-loaded with the purpose, but has minor issues: it begins with 'List Customer AI Corrections — List corrections' which is slightly redundant, and ends with the artifact '[query]' which appears to be a formatting error or incomplete editing. Otherwise, it efficiently conveys usage guidance.

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 simple structure (2 optional string parameters, no nested objects) and absence of output schema, the description adequately covers the primary use cases (list all vs. filter). However, it could improve by briefly describing what constitutes a 'correction' or hinting at the return format to compensate for the missing output schema.

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 valuable usage semantics beyond the schema: it explains the 'no parameters = all results' behavior and advises 'Only use category or query when filtering', clarifying the optional nature and filtering intent of the 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 clearly states the tool 'List corrections for the CUSTOMER-FACING WhatsApp AI', providing specific verb (List), resource (corrections), and scope (customer-facing WhatsApp AI). The 'CUSTOMER-FACING' qualifier effectively distinguishes it from the sibling tool `get_ai_errors`.

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 guidance: 'To list ALL corrections, pass no parameters' and 'Only use category or query when filtering specific results'. This explicitly tells the agent when to use parameters versus when to omit them. It could be improved by explicitly contrasting with `get_ai_errors`.

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