Recommend Books

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds meaningful context about the behavior: it uses the Open Library subject API and sorts results by edition count as a popularity proxy, which helps the agent understand the result ordering. This goes beyond the annotations, earning a 4.

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

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

The description is two concise sentences with the purpose front-loaded and no filler. It efficiently conveys the tool's function and the underlying logic, earning a perfect score.

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

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

An output schema exists, so return values are covered. The description explains the API and sorting, and the schema handles parameter details. It is complete for a read-only recommendation tool, though it does not mention potential edge cases or rate limits, which are minor given the annotations.

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

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

Schema description coverage is 100%, with both 'subject' and 'limit' fully documented in the schema. The description does not add additional parameter semantics, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: recommending books for a subject via Open Library, and explains the underlying API and sorting criterion. It distinguishes itself as a recommendation tool, though it does not explicitly contrast with sibling tools like search_books or get_book, so it falls short of a 5.

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

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

The description implies when to use the tool (when you need popular books on a subject) by explaining the mechanism, but it does not explicitly state when not to use it or mention alternatives. It provides context but lacks explicit routing guidance relative to siblings.

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