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apple-mail-mcp

apple-mail-mcp

by jayvee6
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

summarize_email

Summarize any email message into 2-3 clear sentences using a local AI model. Get a concise plain-text summary of content and required actions from your Apple Mail.

Instructions

Summarize an email in 2-3 sentences using the configured local AI model (LM Studio / Gemma, or OpenAI-compat). Returns a concise plain-text summary of the message content and any required actions.

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": "summarize_email"
  }
}

Input Schema

NameRequiredDescriptionDefault
message_refYesComposite message reference from list_emails or search_emails, e.g. "iCloud::INBOX::msg-id".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are present, so the description carries the full burden. It discloses that the operation uses a local AI model and returns a summary including required actions, which adds some context. However, it does not mention whether the email is modified, what happens if the model is unavailable, or any permission requirements, leaving significant behavioral aspects undisclosed.

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 composed of two concise sentences that front-load the main purpose and immediately state the output format. Every clause adds value, with no redundant or filler content.

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 simple tool with one well-documented parameter and no output schema, the description covers the core aspects: what it does, how it works (local AI model), and what is returned. It does not elaborate on edge cases or side effects, but given the simplicity, it is largely complete. A slight gap is the lack of explicit assurance that no modifications occur.

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 schema already provides 100% coverage for the single parameter 'message_ref', including a clear description and example. The tool description adds no extra semantic meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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 a specific verb ('summarize') and resource ('an email') with a defined output format (2-3 sentences, plain-text summary). It distinguishes itself from sibling tools like get_email and classify_email by focusing on summarization, not retrieval or classification.

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 does not provide explicit guidance on when to use this tool over alternatives. It never mentions conditions like 'use for a quick summary' or 'instead of reading full email', nor does it reference any sibling tools. Usage context is only implied through the purpose.

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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