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Books Like This

by io.github.GoodTurnStudio
OverviewDiscussionsAdmin

Books like one the user enjoyed, or for a genre

find_similar_books
Read-onlyIdempotent

Books like one the user enjoyed, or for a genre. Use for "books like The Martian" or "a good cosy mystery". Suggestions are English-language books for the same readers (picture book, children, young adult or adult), one per author and one per series. Well-read books of the same kind come first (picked_from: shelf, and because names the kind, for example "celebrated dystopian novels"), then books sharing specific subjects on Open Library (picked_from: subjects, and because lists the shared subjects). Novels never get self-help, textbooks, anthologies, study guides, category romance lines or explicit books as suggestions, and popular books are preferred over obscure ones. Other books by the same author are left out unless same_author=true. Give title or subject; common other titles work (1984, The Sorcerer's Stone)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoA book the user liked, 2 to 120 characters.
authorNoThe author's name, to pin down the right book or list their books.
countryNoTwo-letter country code for the shop links, for example US or GB.
subjectNoA genre or theme, for example space opera, cozy mystery, heist.
same_authorNoTrue to include more books by the same author.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, closed-world and non-destructive behavior, yet the description goes far beyond them, disclosing suggestion composition (one per author/series), inclusion rules (picked_from: shelf vs subjects), exclusions (no self-help, textbooks, anthologies, category romance, explicit books), popularity preference, and the same_author override. This is exactly the kind of behavioral context annotations cannot supply.

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-loads the purpose in the first sentence and packs routing, output composition, and caveats into one dense paragraph where nearly every clause carries information. It is slightly long and clause-heavy, but no sentence is wasted, so it stops short of a 5.

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?

With no output schema, the description must characterize the return values, and it does: ordering (well-read first), composition (one per author and series), and provenance (picked_from shelf or subjects). Combined with annotations covering the safety profile, an agent has everything needed to call and interpret this tool correctly.

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

Parameters4/5

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

Schema coverage is 100%, so a baseline of 3 applies, but the description adds meaning beyond the schema: it says common/alternative titles work (

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+resource (

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

Usage Guidelines4/5

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

Provides concrete query examples (

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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