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. While the verb 'Analyse' implies a read-only operation and the 'Returns' section documents the output format ('Ranked list of senders... with email counts'), the description omits explicit safety confirmations, performance characteristics, or side effects that would be necessary for a tool with ambiguous mutability.

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 follows a clear docstring structure (summary, use cases, args, returns) with no wasted words. Information is front-loaded with the core purpose, and the length is appropriate for the 5-parameter complexity. Every sentence earns its place.

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 0% input schema coverage, the description successfully fills all documentation gaps by detailing every parameter. It also explains return values despite the existence of an output schema (which may lack descriptions). For a tool of this complexity (5 flat parameters), the description is sufficiently complete.

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 0% schema description coverage, the description fully compensates by documenting all 5 parameters in the Args section. It provides semantic meaning beyond types (e.g., '0 = all time' for days_back, domain vs individual sender distinction for group_by_domain, and concrete examples like 'Gmail', 'Work' for account).

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 opens with a specific verb ('Analyse') and resource ('mailbox... most frequent senders') that clearly defines the scope. It distinguishes from siblings like get_statistics or search_emails by focusing specifically on sender frequency analysis rather than general email retrieval or broad metrics.

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 concrete use cases ('identifying key contacts, high-volume senders to filter, or newsletter sources'), which helps agents understand when to invoke the tool. However, it lacks explicit exclusions or named alternatives (e.g., 'use search_emails for finding specific messages instead').

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