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kindlemcp

send_to_kindle

Send a document to your Kindle via Amazon's Send-to-Kindle email. Provide Markdown content or a file path (MD, PDF, EPUB) to deliver it to your device.

Instructions

Send a document to the configured Kindle via Amazon Send-to-Kindle email.

Provide exactly one of content or file_path:

  • file_path — path to an existing .md, .pdf, or .epub file to send.

  • content — Markdown text; it is written to a temp file, converted to EPUB with pandoc, and sent.

title becomes the email subject, which Amazon uses as the document name on the Kindle. Returns a human-readable success or error message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
contentNo
file_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses the conversion pipeline (content written to temp file, converted to EPUB with pandoc), that 'title' becomes the email subject/document name, and that a human-readable success/error message is returned. Minor gaps: no mention of prerequisites beyond 'configured Kindle' or potential side effects, but the description is substantially transparent.

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 with a clear first sentence followed by a concise bulleted list. Every sentence adds distinct value: purpose, exclusion rule, per-parameter semantics, conversion detail, and return behavior. No filler or redundancy.

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

Completeness5/5

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

For a tool with three parameters, one required, and a mutual-exclusion constraint, the description covers all necessary operational details: how each parameter works, the file types accepted, the conversion step, and the return value. An output schema exists for the return message, but the description still summarizes it appropriately. No missing information that would hinder correct invocation.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully compensate. It does: 'file_path' is specified as an existing .md/.pdf/.epub file, 'content' is markdown text that gets converted, and 'title' is the email subject/document name. The mutual exclusivity constraint is also clearly explained.

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?

States a specific verb ('Send'), resource ('document to the configured Kindle'), and mechanism ('Amazon Send-to-Kindle email'). Even without siblings, the purpose is immediately clear and the action is distinguished from other potential operations.

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

Usage Guidelines5/5

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

Explicitly instructs the agent to provide exactly one of 'content' or 'file_path', and explains when each is appropriate: existing file vs. markdown text to be converted. This leaves no ambiguity about how to choose between the two mutually exclusive inputs.

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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