Zapmail 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 of behavioral disclosure. It mentions 'built-in patterns' but doesn't elaborate on what these patterns are, whether the tool is idempotent, if it has rate limits, or what the output format looks like (e.g., list of strings). For a generation tool with zero annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence that front-loads the core functionality without unnecessary details. It directly states what the tool does and the input types, making it easy to parse and understand quickly.

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?

For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain the output (e.g., what the generated usernames look like, format, or any constraints), behavioral aspects like error handling, or how it differs from similar tools. This leaves gaps for an AI agent to understand the tool fully.

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, with clear documentation for both parameters ('name' and 'numberOfNames'). The description adds minimal value beyond the schema by implying the input can be a 'full name or single word,' but it doesn't provide additional context like examples or pattern details. Given the high schema coverage, a baseline score of 3 is appropriate.

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's purpose: 'Create professional mailbox usernames from a full name or single word using built-in patterns.' It specifies the verb ('Create'), resource ('mailbox usernames'), and input types ('full name or single word'). However, it doesn't explicitly distinguish this tool from sibling tools like 'generate_name_pairs' or 'generate_domains,' which might have overlapping naming functionality, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools such as 'generate_name_pairs' or 'generate_domains,' which could be relevant for username generation. This lack of contextual usage advice limits its effectiveness for an AI agent.

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