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read_email

Retrieve full email content by email ID. Access detailed message body and headers from macOS Mail through AI agents.

Instructions

Read full email content by ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • server.js:14-14 (registration)
    The 'read_email' tool is registered in a static TOOLS array as ["read_email", "Read full email content by ID"] on line 14.
    ["read_email", "Read full email content by ID"],
  • All tools (including 'read_email') share a stub handler that returns an inspection placeholder message, since the real server is a native binary.
    for (const [name, desc] of TOOLS) {
      server.tool(name, desc, {}, async () => ({
        content: [{ type: "text", text: "This is an inspection stub. Install Local MCP: npx -y local-mcp@latest setup" }],
      }));
  • server.js:106-110 (registration)
    Dynamic registration loop: each tool name and description from the TOOLS array is registered via server.tool() with an empty schema and stub handler.
    for (const [name, desc] of TOOLS) {
      server.tool(name, desc, {}, async () => ({
        content: [{ type: "text", text: "This is an inspection stub. Install Local MCP: npx -y local-mcp@latest setup" }],
      }));
    }
Behavior2/5

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

No annotations are provided, so the description bears the full burden. It does not disclose behavioral traits such as authentication requirements, whether reading marks the email as read, or what 'full email content' includes (e.g., attachments, headers).

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 a single short sentence conveying the core purpose with no unnecessary words.

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

Completeness2/5

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

Given no output schema, the description does not explain the return format or structure. Combined with the parameter schema mismatch, the tool definition is incomplete for an agent to use correctly.

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

Parameters1/5

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

The description mentions 'by ID', but the input schema has no parameters. This creates a contradiction where the description implies a parameter that does not exist in the schema, making it misleading for an AI agent.

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

Purpose4/5

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

The description clearly states the action (read) and resource (full email content) with an identifier (ID). However, it does not differentiate from sibling tools like list_emails or search_emails, which also deal with email retrieval.

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?

No guidance is provided on when to use this tool versus alternatives. It is implied that you should use it when you have an email ID, but there is no explicit instruction or comparison with sibling tools.

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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