Apple Mail 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 disclosure burden. It effectively communicates cross-account aggregation behavior ('across all accounts') and the meta-behavioral trait of generating 'AI suggestions' that 'prompt the assistant to suggest relevant actions.' It also details the return structure. It omits explicit read-only/safety declarations, though 'Get' implies this.

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 appropriately sized with three sentences and a structured Returns section. It is front-loaded with the primary purpose ('Get a comprehensive overview...'). The bullet points within the Returns description are slightly informal but remain readable and purposeful, with minimal redundancy.

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 zero-parameter complexity and existence of an output schema, the description provides sufficient contextual value by explaining what the overview contains (unread counts, folders, AI suggestions) and its cross-account scope. It adequately prepares an agent to interpret results without needing to detail the output schema structure.

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 contains zero parameters, which establishes a baseline score of 4 per the rubric. With no parameters requiring clarification, the description meets the expected standard without needing additional semantic content.

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 states the specific action ('Get a comprehensive overview') and resource ('email inbox status across all accounts'). It distinguishes itself from siblings like `get_mailbox_unread_counts` by emphasizing the cross-account scope and unique 'AI suggestions for actions' feature, though it does not explicitly differentiate from `inbox_dashboard`.

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 implied usage context ('designed to give you a complete picture'), suggesting when to use the tool for comprehensive status checks. However, it lacks explicit 'when-not' guidance or direct comparisons to similar tools like `get_statistics` or `inbox_dashboard` that would help an agent select between them.

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