himalaya-mcp

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read_email

Retrieve the plain-text body of a specific email using its message ID from a listing or search, with optional account and folder selection.

Instructions

Read an email message body (plain text). Use the ID from list_emails or search_emails.

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": {
      "id": "<id>"
    },
    "name": "read_email"
  }
}

Input Schema

Name	Required	Description
`id`	Yes	Email message ID
`folder`	No	Folder name (default: INBOX)
`account`	No	Account name (uses default if omitted)

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv2.2.2Nov 7, 2026

addedInput schema / properties / id / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "maximum": 9007199254740991,
+    "minimum": 0,
+    "type": "integer"
+  }
+]

removedInput schema / properties / id / type
```
Removed value: -"string"
```

First observedv1.7.0Jun 21, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries the full disclosure burden and does contribute that the output is plain-text body content. It is silent on whether reading marks the message as read, how attachments or multipart messages are handled, and what happens if the ID or folder is invalid — notable gaps for an email read operation.

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 short sentences with zero padding, and the core action is front-loaded ahead of the ID-provenance hint. Nothing could be removed without losing information.

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 three-parameter read tool with a fully described schema and no output schema, the description covers purpose, return medium, and ID source adequately. The remaining gap is side-effect behavior on the mailbox, which a single clause could have closed.

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?

Schema description coverage is 100%, with folder and account defaults already documented in the schema, so the baseline is 3. The description adds no additional meaning about the accepted ID forms (string or integer) or which account/folder combinations are valid.

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?

States a specific verb and resource ("Read an email message body") and disambiguates with "(plain text)", which implicitly separates it from siblings like read_email_html, read_email_raw, and render_email. It does not name those siblings explicitly, so the differentiation is inferred rather than stated.

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?

"Use the ID from list_emails or search_emails" tells the agent how to obtain the required identifier, which is genuine prerequisite guidance. However, it never says when to prefer read_email_html or read_email_raw over this tool, so alternative selection is left to inference.

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