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Apple Mail MCP

Apple Mail MCP

by imdinu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_accounts

Retrieve all configured email accounts from Apple Mail to view account names and identifiers for email management tasks.

Instructions

List all configured email accounts in Apple Mail.

Returns: List of account dictionaries with 'name' and 'id' fields.

Example: >>> list_accounts() [{"name": "Work", "id": "abc123"}, {"name": "Personal", "id": "def456"}]

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_accounts"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the return format (list of dictionaries with 'name' and 'id' fields) and includes an example, which adds useful context. However, it lacks details on potential errors, permissions needed, or any side effects, leaving some behavioral aspects unspecified.

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?

The description is well-structured and front-loaded, starting with the core purpose, followed by return details and an example. Every sentence earns its place by providing essential information without redundancy. The example is concise and illustrative, enhancing clarity without verbosity.

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?

Given the tool's simplicity (0 parameters, no annotations, but with an output schema implied by the return description), the description is largely complete. It covers purpose, output format, and includes an example. However, it could improve by addressing potential edge cases or linking to sibling tools for broader context, though the output schema coverage reduces the need for extensive detail.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on output and examples. This meets the baseline for tools with no parameters, as it avoids unnecessary repetition and adds value through output clarification.

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 verb ('List') and resource ('all configured email accounts in Apple Mail'), making the purpose specific and unambiguous. It distinguishes this tool from sibling tools like list_mailboxes (which lists mailboxes rather than accounts) and various email-fetching tools, establishing clear differentiation.

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

Usage Guidelines3/5

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

The description implies usage by specifying what it returns (account dictionaries), but does not explicitly state when to use this tool versus alternatives like list_mailboxes or other email-related tools. No guidance is provided on prerequisites, exclusions, or comparative contexts, leaving usage somewhat inferred rather than clearly defined.

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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