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make_ebook

MAKES an ebook (PDF) from the user's monthly allowance (Pro 10, Premium 20, Ultimate 30 a month; not on Free). Agree the title and topic with the user first, or let them press Make it in the open_product_maker panel. Takes about 20 minutes; answers at once with a job id. Use check_product later, once, rather than polling. Saved in History > Ebooks on digitalmaker.ai.

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": {
      "title": "<title>",
      "topic": "<topic>"
    },
    "name": "make_ebook"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleYes
topicYesWhat the ebook covers and who it is for, one to three sentences.
chaptersNoDefault 7.
author_nameNoOnly if the user gave one.
accent_colorNo
writing_styleNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so: it discloses the consumption cost (Pro 10 / Premium 20 / Ultimate 30 per month, unavailable on Free), the ~20 minute runtime, and that the call returns immediately with a job id rather than the finished artifact. It also names where the result lands (History > Ebooks).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the action and tightly packed — quota, prerequisite, duration, async behavior, polling advice, storage location — with no filler sentences. The pricing-tier parenthetical is dense but earns its place by telling the agent when the call is unavailable.

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 six-parameter, annotation-free, no-output-schema generation tool, the description covers the essentials an agent needs: gating/quota, human confirmation before spending the allowance, the async job-id return, the correct follow-up tool, and expected latency. Little is left to guess before invoking it.

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 coverage is 50%, with topic, chapters and author_name self-documented; the description adds only a procedural constraint on title/topic (agree them with the user first) rather than new meaning. accent_color and writing_style rely entirely on their enum values, which are largely self-explanatory but unexplained in both places.

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?

"MAKES an ebook (PDF)" gives a specific verb plus a specific artifact, immediately separable from siblings like make_spreadsheet, make_website and make_podcast_thumbnail. It also states the scope constraint (user's monthly allowance) that identifies which pipeline this belongs to.

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?

Explicit prerequisite: "Agree the title and topic with the user first, or let them press Make it in the open_product_maker panel" — naming the sibling path as the alternative. It also gives an explicit negative instruction for the follow-up step: "Use check_product later, once, rather than polling."

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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