apple-mail-mcp

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classify_email

Categorize emails, assign priority, and flag action-required messages with tags to streamline inbox triage.

Instructions

Classify an email by category and priority using the configured local AI model. Returns JSON with: category (string), priority (high/medium/low), action_required (boolean), tags (string[]).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "message_ref": "<message_ref>"
    },
    "name": "classify_email"
  }
}

Input Schema

Name	Required	Description	Default
`message_ref`	Yes	Composite message reference from list_emails or search_emails.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.0Aug 4, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description must disclose behavior. It mentions using a configured local AI model and details the returned JSON fields, providing some transparency. However, it does not state whether the operation is read-only, has side effects, or any prerequisites beyond the model configuration.

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

Conciseness5/5

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

Two concise sentences: the first states the action, the second lists the exact output structure. No redundant information, suitable length.

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

Completeness4/5

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

For a tool with one parameter and no output schema, the description covers the core functionality and return values. It lacks explicit mentions of prerequisites or edge cases, but the information provided is sufficient for basic usage.

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

Parameters3/5

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

The single parameter message_ref is fully documented in the schema with a clear description ('Composite message reference from list_emails or search_emails'). The description itself does not add extra meaning beyond the schema, but schema coverage is 100%, so baseline 3 applies.

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 clearly states the tool's function with a specific verb ('classify'), resource ('email'), and detailed output structure (category, priority, action_required, tags). This distinguishes it from siblings like summarize_email or triage_inbox.

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

Usage Guidelines4/5

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

The description implies when to use the tool (when an email needs classification into categories and priorities) but does not explicitly mention alternatives or exclusions. However, the clear context of classification provides sufficient guidance.

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