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pvliesdonk

scholar-mcp

by pvliesdonk

Get Book Excerpt

get_book_excerpt
Read-only

Retrieve a book's publisher description, text snippet, and Google Books preview link by ISBN for quick citation context.

Instructions

Get a book excerpt and preview info from Google Books.

Returns the publisher description, text snippet, and a link to the Google Books preview page. Google Books does not expose full chapter text via API -- the excerpt is a publisher-provided summary and/or search snippet.

An error of not_found means Google Books answered and has no such volume: stop asking about that ISBN. A lookup that never got an answer says so instead, as rate_limited (with retryable) or upstream_error, and is worth retrying -- the book may well exist.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
isbnYesISBN-10 or ISBN-13.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.9.0

TDQS

A4/5.0
Behavior5/5

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

Annotations cover the safety profile (readOnly, non-destructive, open-world), and the description adds substantial behavior beyond them: the API cannot return full chapter text, excerpts are publisher summaries or search snippets, and it enumerates distinct error classes with retry semantics. That is exactly the contextual layer structured fields cannot carry.

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?

It is front-loaded with the purpose and then the output limitation, with the error taxonomy last where it belongs. Slightly long for a one-parameter lookup, but each clause (no full text, error meanings) carries real decision value.

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 single-ISBN read tool with annotations and an output schema present, the description covers the remaining unknowns: what the excerpt actually is and how to interpret each error state. Nothing an agent needs to call or interpret this tool is missing.

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% with a single 'isbn' parameter already described as 'ISBN-10 or ISBN-13,' so the schema does the heavy lifting. The description adds no further parameter detail, making the 3 baseline appropriate.

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?

The description names a specific verb and resource ('Get a book excerpt and preview info from Google Books') and clarifies what the payload contains (publisher description, snippet, preview link). It implicitly separates the tool from get_book/search_books by framing it as excerpt-only, but never names those siblings explicitly.

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?

It gives useful conditional guidance on outcomes ('not_found' means stop asking about that ISBN; 'rate_limited'/'upstream_error' are worth retrying), which effectively tells the agent how to react. However, there is no guidance on when to choose this over get_book or search_books, so context 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.