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isityael

Apple Mail MCP Server

by isityael

get_statistics

Analyze email activity by account, sender, or mailbox with customizable time range for actionable insights.

Instructions

Get comprehensive email statistics and analytics.

Args: account: Account name (e.g., "Gmail", "Work") scope: Analysis scope: "account_overview", "sender_stats", "mailbox_breakdown" sender: Specific sender for "sender_stats" scope mailbox: Specific mailbox for "mailbox_breakdown" scope days_back: Number of days to analyze (default: 30, 0 = all time)

Returns: Formatted statistics report with metrics and insights

User Preferences: {"workflow": "default", "notifications": "enabled"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoaccount_overview
senderNo
accountYes
mailboxNo
days_backNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits. It only describes parameters and output, but does not indicate whether the tool is read-only, if it requires specific permissions, or any side effects. The return value is vaguely described as 'formatted statistics report,' but no details on data freshness, limits, or analogous behaviors.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a structured docstring with clear sections (Args, Returns). It is concise, with only one introductory sentence and minimal repetition. Minor improvement: some parameter explanations (e.g., 'Specific sender for...') could be integrated into parameter descriptions rather than repeated inline.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (5 params, 1 required) and presence of an output schema, the description adequately covers purpose and parameters. However, it lacks behavioral and usage context (e.g., whether it's safe to call frequently, any prerequisites like account existence). This gap in completeness is notable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries full burden for parameter meaning. It explains all 5 parameters: account (placeholder), scope with enumerated values, sender and mailbox conditioned on scope, and days_back with default. This adds essential context missing from the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Get comprehensive email statistics and analytics,' which is a specific verb+resource pair. It further distinguishes via the 'scope' parameter (account_overview, sender_stats, mailbox_breakdown), clearly differentiating from sibling tools like get_unread_count or get_inbox_overview.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lacks explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions (e.g., for simple counts use get_unread_count), or context where this tool is appropriate, leaving the agent to infer usage from the parameter names.

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

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