Books Like This

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.

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.

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.

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.

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.

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.

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description's only extra behavioral detail is the 'newest first' result ordering, which is genuinely useful but leaves pagination and result limits undisclosed.

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?

Two short sentences, zero filler, with the scoping behavior front-loaded and examples immediately after. Every clause earns its place.

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?

For a simple read-only author lookup with full schema coverage and no output schema, the description plus annotations supply everything needed to call it correctly. Only result-size/pagination behavior is unaddressed, which is a minor gap.

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%, so both 'author' and 'country' are already documented in the schema, including the country enum list and its buy-link purpose. The description adds no parameter-level detail beyond that baseline.

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 states a specific verb and resource ('An author's books') and adds a scope qualifier ('newest first'), so the agent knows the ordering behavior up front. It doesn't explicitly contrast with siblings find_similar_books or get_book, but the resource is distinct enough to avoid confusion.

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 two example queries ('what has Andy Weir written?', 'Sally Rooney's books') give concrete trigger contexts that map directly to the tool's intent. There is no explicit when-not or named alternative, but the usage scenario is unambiguous for a lookup-by-author tool.

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

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, and the description still adds real behavior: the screen_checked=false caveat explains a partial/unknown-result state that no annotation conveys. 'Misspellings are fine' also discloses fuzzy-match tolerance. It omits how results are shaped, but that is minor given the annotation coverage.

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 capability statement is front-loaded in the first sentence, followed by compact examples and the one conditional caveat. The example list is slightly longer than strictly necessary but each example maps to a distinct capability (details, adaptation status, length).

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?

With no output schema, the description carries the return-value burden and does cover the main payload (book details, film/series status) plus the degraded-result case via screen_checked. Missing is any mention of how contradictions or no-match cases are surfaced, but for a single-entity lookup this is close to complete.

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%, so the schema already documents title length limits, the author pinning use, and the country enum values for buy links. The description adds only marginal semantics ('Misspellings are fine' for title, and no guidance on country defaults), so the baseline 3 applies.

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 first sentence names a concrete verb+resource (book details) plus the distinctive extra (film/TV adaptation status), which is more specific than a generic 'get' tool. It does not explicitly differentiate itself from find_similar_books or get_author_books, so an agent must infer it is the single-book detail lookup from the name/title alone.

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?

Three concrete example utterances ('tell me about Project Hail Mary', 'is The Name of the Wind being made into a film?', 'how long is Dune?') make the intended use case unambiguous and cover both queries the tool serves. There is no explicit statement of when NOT to use it or when to prefer the two sibling tools, which keeps it short of a 5.

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