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calculate_book_spine_width

Calculate exact spine thickness, wrap dimensions, and spine text eligibility for Amazon KDP, offset, or hardcover books from page count and paper type.

Instructions

Calculates exact book spine thickness, wrap dimensions, and spine text eligibility for Amazon KDP, commercial offset, or hardcover bindings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bleed_mmNoOuter document bleed in mm (standard 3.175mm / 0.125 inches for KDP).
page_countYesTotal page count of the book (must be an even integer).
paper_typeYesPaper substrate stock and weight grade.
binding_typeNoBinding format: paperback (perfect bound) or hardcover (casebound with board allowance).paperback
trim_width_mmNoTrimmed page width in mm (e.g. 152.4 for 6x9 inch, 210 for A4, 148 for A5).
trim_height_mmNoTrimmed page height in mm (e.g. 228.6 for 6x9 inch, 297 for A4, 210 for A5).
custom_caliper_mmNoOptional custom single-sheet caliper thickness in millimeters (required if paper_type is 'custom').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations, so the description must carry the full burden. It reveals that it computes three outputs (thickness, wrap dimensions, text eligibility) and supports multiple binding types, but does not explain return format, units, or how custom paper is handled beyond the schema.

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?

A single, front-loaded sentence with no wasted words. It is appropriately sized, though it could be tightened or expanded slightly for clarity without loss.

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

Completeness3/5

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

For a calculation tool with 7 parameters and no output schema, the description is minimally adequate: it names the computed outputs and binding types but omits units, expected input ranges, and what the outputs contain. It should do more given the lack of annotations and output schema.

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 100%, so the schema already documents all parameters with enums and defaults. The description adds no additional parameter meaning beyond listing the outcomes; baseline 3 is appropriate when schema does the heavy lifting.

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?

Clearly states a specific verb (calculates) and resources (spine thickness, wrap dimensions, spine text eligibility), with binding platforms named. Distinguishes from siblings like convert_paper_units and calculate_sheet_imposition_yield, though no explicit exclusions are given.

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 when-to-use guidance, prerequisites, or mention of alternatives. The description only states what it does, leaving the agent to infer context from the name and parameters.

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