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

Even with annotations declaring read-only, open-world, and idempotent behavior, the description adds substantial value: it reveals that the tool resolves the primary author, ranks by popularity, returns specific fields, paginates results, and caps limit at 100. These are non-obvious behaviors an agent needs to predict output.

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 compact and front-loaded: the purpose appears in the first sentence, and the following two sentences add only high-value behavioral details. No filler, no repetition of annotations or schema.

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?

Combined with the annotations and output schema, the description covers input semantics, output contents, pagination, and the limit cap. There is nothing essential missing for an agent to invoke the 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 description coverage is 0%, so the description must compensate. It clarifies that book_id is a book identifier (not an author ID) and that limit is capped at 100 beyond its schema default. The id format and accepted values are not given, but core semantics are well covered.

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 ('List') and resource ('an author's works/bibliography') and makes the unique input clear ('given any of their books'). This distinguishes it from siblings like similar_books or series_books without needing to inspect their schemas.

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 trigger condition 'given any of their books' is clear, but the description never mentions alternatives or exclusion criteria. An agent must infer when to prefer author_books over similar_books or series_books, so guidance is implied rather than explicit.

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=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral detail beyond annotations: results are paginated in batches, the limit is capped at 100, and the ordering is by popularity. These are not inferable from the annotations or schema, making the description genuinely informative.

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 compact and front-loaded. The first sentence states the core purpose and ordering, followed by a sentence on output fields and use cases, then a final sentence on pagination and cap. Each sentence earns its place—no fluff or redundancy—and the structure allows an agent to quickly grasp what the tool does and its constraints.

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 list tool with two parameters and an existing output schema, the description is thorough. It covers the result fields (title, votes, book count, URL), the use case, the pagination behavior, and the cap. It does not need to explain return formats because the output schema handles that, and the annotations already cover safety, so nothing critical is missing.

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?

The input schema has 0% description coverage, placing the burden on the description. While the description implicitly references book_id (the book in question) and mentions that 'limit is capped at 100,' it does not explicitly explain that limit controls the number of lists returned per batch, nor does it mention the default value of 10. This is partial compensation but not complete.

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 explicitly states the verb ('List'), the resource ('the Listopia lists a book appears on'), and the ordering ('by popularity'), with an example. This clearly distinguishes it from siblings like get_book (which fetches a single book) and similar_books (which suggest related works) without needing to open schemas.

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?

It provides clear context on when to use the tool ('Good for "what kind of book is this / what's it grouped with" and for discovery'), which guides an agent toward appropriate scenarios. It does not explicitly name alternatives or state when not to use it, but the purpose is specific enough to avoid confusion with most siblings.

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?

Beyond the readOnly/idempotent annotations, the description discloses the ranking logic (best-to-worst by average rating), the computed percentages (pct_positive, pct_critical), and the refusal behavior for >10 ids. These are operational details an agent could not infer from the annotations or schema. No contradiction exists.

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 three sentences, each with a distinct job: purpose, return semantics, and call limits. The most important information is front-loaded. No sentence is filler.

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 one-parameter read-only tool with an output schema already available, the description covers invocation limits, return semantics, and the source of ids. Nothing needed for a correct call is missing. The output schema removes the need to enumerate returned fields in prose.

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?

The input schema only provides a type and title for book_ids with 0% description coverage, so the description carries the explanatory burden. It supplies the valid count range (2-10), the refusal behavior, and the provenance ('from search_books etc.'). It does not define the exact id format, but for a single string-array param this is sufficient.

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 opening sentence uses a specific verb ('Compare') and resource ('books') and immediately narrows the scope to rating and rating distribution. The second sentence specifies a ranked best-to-worst output, which differentiates it from sibling tools like get_book or similar_books. It even points at sibling search_books for id provenance, so an agent can select it confidently.

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 clearly states the intended call pattern: pass 2-10 book ids, and gives a hard behavioral rule for exceeding that range ('more than 10 is refused rather than silently trimmed, so split the call'). It does not explicitly enumerate when-not-to-use alternatives, so it falls just short of a full 5. Still, the context for when this tool is appropriate is unambiguous.

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 mark this as read-only and idempotent, so the safety profile is established. The description adds meaningful behavioral detail beyond annotations: it parses __NEXT_DATA__ JSON rather than scraping the DOM, which affects robustness, and it enumerates exactly what data is returned. This gives an agent useful expectations about how the tool behaves and why it is reliable.

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 organized into three compact, purposeful sections: the core action, the notable behavior/content, and a citation instruction. Every sentence adds information; there is no filler or repetition of schema fields. The technical detail about __NEXT_DATA__ is justified because it explains robustness rather than being extraneous.

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?

Given the rich annotations, an output schema, and only two parameters, the description covers all necessary ground: it explains what data is included, how parameters behave, how the tool parses pages, and how to use the returned 'url'. Nothing an agent needs to invoke or interpret this tool correctly is missing.

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 0%, so the description must fully explain parameters, and it does. It details the accepted book_id formats (numeric or numeric-slug with an example) and specifies review_language_limit's default and maximum. This compensates completely for the lack of schema-description text.

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 opens with a specific verb and resource: 'Get full details for a book by its Goodreads id.' It also disambiguates from siblings by listing what the tool includes (ratings histogram, series memberships, review-language breakdown) and explicitly hands off review text to get_reviews. This is enough for an agent to know exactly what this tool is for.

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 explicitly directs agents to use get_reviews when actual review text is needed, which is a clear usage boundary. It also explains the effect of review_language_limit and instructs agents to link to the book's 'url' when citing details. It does not broadly cover when to choose this over search_books or similar_books, but the context is clear enough for typical selection.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral detail beyond that: results paginate in batches and limit is capped at 100. This is useful operational context not present in annotations.

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 sentences, front-loaded with the core action and followed by usage guidance and a pagination caveat. No filler.

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 annotations covering safety/idempotency and an output schema present, the description covers purpose, usage, and pagination behavior. Nothing critical is missing for an agent to call it 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 has 0% description coverage, so the description carries the burden. It clarifies that limit is capped at 100 and that results include formats/ISBNs/publishers/dates, which indirectly informs book_id output. However, it doesn't explicitly document the book_id parameter format or how limit interacts with pagination.

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 opens with a specific verb ('List') and resource ('published editions of a book'), then enumerates the data fields (formats, ISBNs, publishers, dates). This clearly distinguishes it from sibling tools like get_book (single book metadata) and search_books (search).

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?

It states an explicit use case: 'which edition / format / ISBN' questions. It doesn't name alternatives or exclusions, but the context is clear enough for an agent to select this tool over siblings.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context beyond annotations: true pagination allowing limit to exceed ~30, server-side star filters, spoiler exclusion behavior, the 'has_more' flag, and the cap at 100 to 'stay polite.' It also discloses that paging is capped and that spoiler-heavy books can return fewer than limit. This is rich, non-obvious behavioral disclosure.

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 well-structured with a clear opening sentence, a paragraph on backend behavior, and a bullet-like breakdown of parameters. It is slightly longer than strictly necessary, but every sentence adds value—pagination, ordering, aggregation, and parameter semantics are all covered. The front-loading of the core purpose ('actual review text, not just a score') is effective.

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?

Given the tool's complexity (5 params, 0% schema coverage, no enums), the description is remarkably complete. It covers what the tool returns, how pagination works, how filters behave, the meaning of 'has_more', and the cap on limit. The output schema exists, so return values don't need to be enumerated. An agent has everything needed to invoke this tool correctly and interpret its results.

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 0%, so the description carries the full burden of explaining parameters. It explains limit (max reviews, capped at 100), min_rating/max_rating (server-side star filters with examples like min_rating=4 for positive reviews), and exclude_spoilers (drop flagged reviews). It also explains the interaction between exclude_spoilers and pagination. This fully compensates for the lack of schema descriptions.

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 ('Get') and resource ('reader reviews for a book'), and immediately distinguishes itself from a mere score by noting it returns 'the actual review text, not just a score.' It also clarifies it fetches from Goodreads' GraphQL backend, aggregates across editions, and returns reviews in 'most relevant' order, which clearly differentiates it from siblings like get_book or get_editions.

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 provides clear context on when to use this tool: when you need review text, not just a score. It explains pagination behavior, the 'has_more' flag, and the effect of filters like exclude_spoilers. It doesn't explicitly name sibling alternatives or state when not to use it, but the context is strong enough for an agent to select it appropriately.

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?

Beyond the readOnly/idempotent annotations, it discloses the RSS feed source, ~100-item page size, page parameter behavior, default user configuration, and the 'link' field instruction. These are concrete execution details an agent needs and cannot infer from annotations alone.

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?

Four concise sentences with the core operation front-loaded. Every sentence adds useful information with no filler or repetition.

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 read-only list operation with zero required parameters and an output schema present, this describes access constraints, pagination, defaults, and citation behavior. Nothing essential appears missing for correct invocation.

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 0%, but the description compensates well by explaining shelf values, page numbering, and the default user. It leaves a small gap by not explicitly stating that user_id can override the configured default.

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 the specific action 'List books on a shelf' and the access method 'via its RSS feed', which clearly distinguishes it from siblings like list_shelves. It does not explicitly name alternatives, but the resource and scope are unambiguous.

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 clear operational context: public shelves only, no auth, common shelf names, pagination starting at 1, and the default user. It does not explicitly describe when to prefer this tool over sibling tools, so it does not reach a 5.

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 mark the tool as read-only and idempotent, and the description adds valuable behavior beyond that: the data is scraped and best-effort, private profiles raise LoginRequired, and very long shelf lists may be capped. These edge cases are non-obvious and useful.

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 compact and front-loaded: the core purpose is in the first sentence, followed by short, relevant caveats about scope, failure mode, and limitations. Every sentence adds unique information with no filler.

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 optional-parameter read-only tool with an output schema, the description covers purpose, default behavior, shelf scope, relation to get_shelf, failure behavior, and a known limitation. Nothing essential is missing for an agent to invoke it 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?

The only parameter, user_id, has no schema description, so the description carries the burden. It explains the default behavior ('Defaults to the configured user') and implies that supplying user_id targets another profile. It does not spell out the expected user_id format, leaving a minor gap.

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 opens with a specific verb and resource: 'List a user's shelf names.' It further clarifies scope by distinguishing exclusive shelves from custom shelves and ties the output to get_shelf's 'shelf' argument, making the tool's role distinct from siblings.

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?

It provides clear operational context: defaults to the configured user, covers all shelf types, and explicitly connects the result to get_shelf. It does not explicitly state when not to use this tool or name alternatives, so it stops 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.

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

Annotations already establish safe read-only, idempotent behavior; the description adds the ranking metric (member-add counts), the page equivalent, the 50-item cap, and the return-field structure including chaining hints. No contradictions with annotations.

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 well structured: a lead sentence stating purpose, a concise explanation of the ranking logic, a compact parameter list, and a brief note on return fields. Every sentence adds value and there is no filler or tautology.

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?

All three parameters are explained, the optional month behavior is clear, and the return content is summarized even though an output schema exists. An agent has enough information to call this tool correctly without consulting external documentation.

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?

The input schema has 0% description coverage, but the description fully compensates: it defines year as a 4-digit release year, month as an optional 1-12 value versus null for the whole year, and limit with its cap. This aligns perfectly with the schema's defaults and nullability.

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 opens with a specific, non-generic purpose: ranking books/works by release date according to Goodreads member additions, and it names the exact page it mirrors. This clearly distinguishes it from sibling tools like search_books or get_shelf by centering on the popularity-by-date dimension.

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 gives clear context on when to use it: for a given release year, optionally narrowed to a single month, with a deterministic cap on result size. It doesn't name alternative tools or exclusions, but the conditions for calling it are unambiguous.

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 mark it read-only and idempotent; the description adds non-obvious implementation behaviors: 'JSON autocomplete endpoint (no auth, no HTML parsing)' and the hard cap of roughly '~5 matches' regardless of max_results. It also lists return fields, giving the agent accurate expectations. No contradiction with annotations.

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 compact and front-loaded: purpose first, then implementation/return behavior, then the max_results caveat. Every sentence earns its place and there is no filler.

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 two-parameter read-only search, the description covers auth, parsing behavior, return contents, and result cap. The presence of an output schema means return-value details do not need to be repeated, and no invocation-critical information is missing.

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?

The schema provides no property descriptions, so the description must compensate. It gives query meaning ('by title/author/ISBN') and fully explains max_results, including that larger values do not increase results beyond the endpoint's ~5-match limit. This is sufficient, though query syntax could be more detailed.

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 opens with a specific verb and resource: 'Search Goodreads for books by title/author/ISBN.' This clearly differentiates it from siblings like get_book (specific book) or author_books (author-scoped) by emphasizing broad search.

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?

It establishes a clear use case: finding books by title, author, or ISBN. It does not name alternatives or state when not to use it, but the context is sufficient for an agent to select it over more specific sibling tools.

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?

Adds behavioral detail beyond annotations: pagination in batches, limit capped at 100, and semantics of returned fields (placement and is_primary). No contradiction with readOnly/idempotent annotations.

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 compact paragraphs, front-loaded with the core action and structured with parameter guidance and output details. No filler or redundant repetition of annotations.

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?

The description covers purpose, disambiguation, output field semantics, and pagination constraints. With the output schema available and read-only annotations, an agent has everything needed to call the 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 has 0% description coverage, but the description explains all three parameters: book_id is the input book in the series, series_index is zero-based and references get_book's series_memberships order, and limit is capped at 100. This fully compensates for the schema gap.

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 the precise verb 'List' with resource 'books in a series' and a distinguishing attribute: reading-order placement. Clearly distinguishes from sibling tools like author_books and similar_books by focusing on a given book's series membership.

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?

Explains when to use it: given any book in a series, list its books; also gives a specific conditional: when a book belongs to multiple series, pass the zero-based series_index from get_book's series_memberships order. Does not explicitly rule out alternatives but provides clear context and a concrete workflow referencing get_book.

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, openWorldHint, idempotentHint, and destructiveHint=false. The description adds useful behavioral details beyond those: results paginate in batches, limit is capped at 100, and each result includes book_id/title/author/rating/url. This is meaningful context without contradicting annotations.

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?

Three sentences with no wasted words. The purpose is front-loaded, and the additional details about result fields, chaining, pagination, and limit cap each earn their 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 two-parameter read-only tool with an output schema, the description is complete. It covers the data source, result contents, chaining opportunities, pagination, and limit cap. Nothing essential is missing for an agent to invoke it 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 description coverage is 0%, so the description must compensate. It explains the limit cap and pagination behavior, but does not elaborate on book_id semantics beyond 'the given one.' The default limit is already in the schema, so the description adds some but not full parameter clarity.

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 returns books similar to a given one, using Goodreads' own recommendation graph. This distinguishes it from web search and implies a specific resource and behavior. The 'Readers also enjoyed' framing makes the purpose immediately recognizable.

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?

It gives clear context for when to use the tool: when Goodreads' recommendation graph is needed, and notes that it is hard to reproduce via web search. It also suggests chaining into get_book or get_reviews, but does not explicitly name alternative sibling tools or when not to use them.

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