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scholar-mcp

scholar-mcp

by pvliesdonk

Recommend Books

recommend_books
Read-only

Finds books on a given subject via Open Library and ranks results by edition count to indicate popularity and support reading discovery.

Instructions

Recommend books for a subject via Open Library.

Uses the Open Library subject API to find popular books on a topic, sorted by edition count (a proxy for popularity).

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum results to return (max 50).
subjectYesSubject or topic, such as ``machine learning``, ``algorithms`` or ``computer vision``.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.1.1
    • changedInput schema / properties / subject / description
      Previous value: -"Subject or topic (e.g. \"machine learning\",\n\"algorithms\", \"computer vision\")."New value: +"Subject or topic, such as ``machine learning``,\n``algorithms`` or ``computer vision``."
  2. First observedv1.9.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds real value beyond that by disclosing the ranking mechanism (edition count as a popularity proxy) and the underlying subject API, but says nothing about failure modes, rate limits, or how sparse subjects are handled.

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?

Two short sentences, front-loaded with the action and the source, with no filler. There is mild redundancy between "via Open Library" and "Uses the Open Library subject API", which is the only wasted phrasing.

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

Completeness4/5

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

With only two fully documented parameters and an output schema present, the description need not explain return values, and it covers the essential behavior: source, ranking logic, and topic scope. Only the relationship to the sibling recommendation/search tools is left unaddressed.

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 the baseline is 3, and the description goes slightly beyond by clarifying that 'subject' is a topic fed into the subject API and that results are popularity-ranked rather than arbitrary. The 'limit' cap is left entirely to the schema, which is acceptable given full coverage.

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?

States a specific verb and resource ("Recommend books for a subject") plus the upstream source (Open Library), which is unambiguous on its own. It does not distinguish itself from the nearby siblings search_books or recommend_papers, so an agent must infer the difference from the ranking note alone.

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?

Usage is implied by "find popular books on a topic" and "sorted by edition count", which hints this is a popularity-ranked recommendation rather than a keyword search. There is no explicit when-to-use guidance and no mention of when search_books or recommend_papers would be the better choice.

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