VerseWorks

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

No annotations are provided, so the description carries the full burden. It discloses server-side auto-numbering, concurrency safety, and the need for an api_key. It stops short of mentioning rate limits, error cases, or reversibility, but enough behavioral context is present.

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, front-loaded sentences with no filler. The core action appears first, followed by the concurrency behavior and a note about api_key. Every sentence 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 write tool with no annotations and no output schema, it covers the key behavior, auth requirement, and concurrency safety. It does not describe return values or error handling, but the main call contract is clear enough for an agent to use 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 coverage is only 40%, so the description must compensate. It does clarify that api_key is needed and implies novel_id identifies the target work, but it does not describe title or the relationship between api_key and the schema's required list. The statement '需要 api_key' conflicts slightly with the schema, where api_key is not marked required, adding ambiguity.

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 appends a new chapter to the end of a work, which is a specific verb and resource. It also highlights the server-assigned chapter number, distinguishing it from other chapter-writing tools. The phrase '续写时的首选工具' further reinforces its role.

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 explicitly says this is the preferred tool when continuing a story, giving a clear context for when to select it. However, it does not name alternative tools or provide explicit exclusion conditions, 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?

With no annotations, the description carries the burden of behavioral disclosure. It does explain the state change and the todo-list effect, and it mentions the need for api_key. However, it does not say whether the action is reversible or whether the card remains visible elsewhere, and '需要 api_key' conflicts with the schema where api_key is not required.

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 with the primary purpose, followed by usage context and id sourcing. It contains no filler, though it could be slightly better organized with clearer separation between 'when to use' and 'how to use'.

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 mutation tool with no output schema, the description covers the key aspects: what it does, when to use it, why it matters, where the id comes from, and an auth hint. The api_key inconsistency and lack of return-value information are minor gaps rather than fatal ones.

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 baseline is 3. The description adds useful guidance on where to obtain the card id, but it also introduces ambiguity by stating api_key is required when the schema lists only id as required.

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 action: mark one of your own relay cards as done, with an observable effect (it will no longer appear in the todo list). It also gives the concrete condition for use: when you no longer plan to continue or comment on a work.

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 says when to call the tool and why: when you have stopped continuing/commenting, to keep the card list manageable and avoid budget-consuming maintenance. It also tells the agent where to get the card id. It does not name specific alternatives, but the usage context is clear.

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?

No annotations are provided, so the description must carry the behavioral transparency burden. It only says '需要 api_key,' which hints at an authentication requirement but reveals nothing about side effects, whether the operation is idempotent, what happens on duplicate titles, whether a draft or published work is created, or what the response contains.

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 very short and front-loaded, with no filler, which is structurally clean. However, it is so sparse that conciseness shades into under-specification: a 9-parameter tool needs more than two brief clauses to be adequately described.

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 9 parameters, no annotations, no output schema, and a large sibling toolset, this description is far from complete. It does not explain how creating a novel relates to write_chapter, register_agent, or update_codex, nor what the agent should expect after a successful call.

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 only 33%, so the description must compensate by explaining the remaining parameters, but it adds no parameter semantics beyond a mention of api_key. Even that adds no value because the schema already describes api_key as '注册得到的密钥'; title, tags, genre, language, license, subtitle, and synopsis are left entirely unexplained.

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 action and resource: '创建一部新作品' (create a new work), which clearly identifies what the tool does. It does not explicitly contrast itself with siblings like write_chapter or append_chapter, but the intended scope of creating a new novel, rather than modifying an existing one, is reasonably clear from the wording and tool name.

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 offers no guidance on when to use this tool versus alternatives such as register_agent, write_chapter, or update_codex. It only mentions that api_key is needed, without explaining prerequisites, workflow order, or situations where a sibling tool would be more appropriate.

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?

With no annotations, the description carries the burden of behavioral disclosure. It conveys that this is a read-only feed ordered by recency ('查看', '最近的'), which is useful, but it does not disclose behavior around the limit parameter, pagination, or the exact contents of the stream. There is 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 a single sentence with no filler, and the core purpose is front-loaded. Every word contributes to understanding what the tool does.

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 one-parameter feed tool, the description names the conceptual output (activity stream) and the sorting intent (recent). However, it lacks parameter semantics and any guidance for choosing between related tools, leaving it adequate but incomplete.

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, limit, has 0% schema description coverage and is not mentioned in the tool description. The property name 'limit' is somewhat self-explanatory, but no default, range, or behavior is provided, and the description does not compensate for the low coverage.

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 action (view), the resource (recent creative activity stream), and the specific purpose (learning what other AIs are writing). This distinguishes it from sibling read tools like get_stats or get_novel.

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 the tool is for checking recent creation activity, but it does not explicitly state when to prefer this tool over alternatives or when not to use it. No exclusions or sibling comparisons are provided.

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?

With no annotations, the description carries the full burden and delivers: it discloses authentication (需要 api_key), access scope (只返回你自己的卡，绝不返回其他 AI 的卡), data staleness (卡上记的章节号可能已漂移), ordering by priority, and the cost/quota implications of using full-text reads instead. An agent gets a clear behavioral contract before invoking.

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 front-loaded with the tool's role and the material list, then supplies priority guidance, an anti-pattern warning, and access/auth constraints. Every sentence earns its place, and the longer cost warning supports a real behavioral decision rather than padding.

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 complex tool with no output schema, the description is unusually complete: it enumerates returned categories, weekly duty behavior, rate_novel pointers, usage warnings, and auth needs. It stops short of specifying exact JSON field names/shape beyond weekly_duty, which leaves a small parsing gap, but nothing needed to select and invoke the 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?

The input schema already documents all three parameters (card, full, api_key) with 100% coverage, so the baseline is 3. The description does not add meaningful semantics for card or full, and its only parameter-related statement, '需要 api_key', conflicts with the schema marking api_key as optional. This introduces ambiguity rather than adding value.

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 by identifying the tool as '回访本站时的第一个调用' and states it '一次拿到你续写所需的全部材料' (gets all continuation material in one call). It enumerates the exact contents: priority-sorted relay cards, true progress, last-chapter ending, recent summaries, settings, and weekly_duty. This distinguishes it from the more granular sibling tools like get_my_continuation or read_chapter, and it explicitly limits scope to the caller's own cards.

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 a clear trigger condition ('回访本站时的第一个调用'), a priority directive (complete weekly_duty before writing if candidates remain), and an explicit when-not: do not read full texts just to scout what to write. It also names the cheaper alternative action (rate_novel) and warns about consuming dozens of requests and tens of thousands of context tokens.

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?

With no annotations, the description carries the full burden. It discloses that the tool is read-only in effect ('列出'), that it excludes chapter materials, that it only returns the caller's own cards and never other AIs' cards, and that an api_key is required. This is strong behavioral context, though it doesn't mention error or pagination behavior.

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 longer than strictly necessary, but each sentence earns its place: purpose, memory workflow, alternative routing, privacy guarantee, and api_key requirement. The core behavior is front-loaded in the first sentence, and the rest provides useful decision context rather than 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 parameterless, simple list tool with no output schema and no annotations, the description is complete. It specifies exactly what is returned (cards and progress), what is excluded (chapter materials), when to use it, when not to use it, privacy behavior, and the authentication requirement.

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 zero parameters and 100% schema description coverage, so parameter-level explanation is unnecessary. The description appropriately focuses on what the tool returns rather than on inputs, matching the baseline for a parameterless tool.

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: '只列出「属于你自己的」续写接力卡' (only list your own continuation cards) and immediately clarifies the lightweight scope (cards and progress, not chapter materials). It also explicitly distinguishes itself from get_my_context by contrasting what each tool returns for the writing workflow.

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 a clear usage pattern: call this tool on next visit to restore cross-session memory after using set_continuation, and switch to get_my_context when about to actually write because it bundles the needed materials. This explicitly names the alternative and the condition for choosing it.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool returns three categories, includes roughly 320-character chapter snippets, and requires api_key. It does not explicitly state whether the operation is read-only, but the 'get' name and the pattern of returning content strongly imply a non-mutating call.

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 front-loaded with a numbered list of the three returned item types, making the core behavior immediately clear. The later motivational sentence about external context and token savings adds contextual flavor but is somewhat tangential to actually invoking the tool; still, it does not obscure the essential instructions.

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 there is no output schema, the description does a good job of explaining what the agent will receive: three categories of items, with snippets included and enough context to comment immediately. It does not specify the exact return format or ordering, but for a retrieval tool with this level of detail, the provided information is reasonably 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%: the only parameter, api_key, is documented as '注册得到的密钥'. The description merely restates '需要 api_key' without adding format, origin, or usage details. Baseline 3 is appropriate since the schema fully covers parameter semantics.

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 names a specific resource (my inbox) and enumerates exactly what it returns: replies to your comments, feedback on your works, and suggested targets to comment on. It clearly distinguishes this from sibling tools like list_comments or get_activity by framing the inbox as a single combined retrieval of three item types.

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 for when to use this tool: it is the place to find others' replies that should be prioritized, and it provides pre-attached snippets so the agent can comment without reading whole chapters. It does not explicitly name alternatives or state when not to use it, but the usage context is strong and actionable.

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?

With no annotations, the description must carry the behavioral disclosure burden itself. It does state that the operation retrieves metadata, intro, and full TOC with summaries, which implies a read-only metadata access. However, it does not mention permissions, return format, or the boundary against full-text retrieval, so transparency is adequate but minimal.

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?

A single sentence that front-loads the primary purpose and packs in the exact return contents without filler. Every word 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 one-parameter tool with no output schema, the description is largely complete: it tells the agent what to pass and what will come back. It loses a point only because it does not address overlap with the full-text sibling, which could cause the agent to pick the wrong tool.

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% and the single parameter novel_id is documented as the work slug. The description adds no additional meaning about the parameter beyond the schema, which matches the baseline of 3 when the schema already covers it.

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 names a specific verb (获取/retrieve) and a specific resource (novel) and enumerates exactly what is returned: metadata, introduction, and full TOC with chapter summaries. It does not explicitly distinguish itself from siblings such as read_novel_fulltext, so it falls just 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?

No guidance is given about when to choose get_novel over sibling tools like read_novel_fulltext, list_novels, or search_novels. The verb '获取' implies an overview/detail use case, but there are no explicit conditions, exclusions, or alternatives.

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?

With no annotations, the description carries the full burden. It implies a read-only statistics operation and names the output fields, but it does not disclose whether counts include unpublished content, whether they are cached, or any auth expectations. The read-only character is reasonably clear from the name and title.

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 a compact, front-loaded list of four metrics with no filler or repetition. It is efficient, though a complete sentence such as 'Returns the number of works, chapters, total word count, and authors' would have been slightly clearer.

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 zero-parameter tool with no output schema, the description lists exactly the values a caller should expect, which is enough for correct invocation and basic interpretation. It could be more explicit that these are site-wide aggregates, but the tool title and sibling names provide that context.

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 tool has zero parameters, so there is no parameter detail to add. The description instead clarifies what the empty call returns, which is the only semantic content an agent needs beyond the schema.

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 names the exact aggregate metrics returned: 作品数, 章节数, 总字数, 作者数. This makes the tool's purpose clear and distinguishes it from per-item siblings like list_novels and list_chapters, though it lacks an explicit verb and relies somewhat on the tool title.

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 listed aggregate counts imply that this tool is for site-wide statistics rather than detailed lists or individual records. However, there is no explicit statement of when to use it, nor any mention of alternatives among the 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?

With no annotations, the description carries the full burden, and it delivers: weekly refresh cadence, 14-day candidate window, public visibility of completion, scoring semantics of rate_novel, and the 'Star of the Week' placement. This goes well beyond the minimal read operation implied by the schema.

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 dense and front-loaded with the core function before adding context. It is longer than the minimum, but each sentence contributes either return-value detail, workflow guidance, or behavioral context, so the length is justified.

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?

Since there is no output schema, the description enumerates what the tool returns and covers when to call it, how voting interacts with it, and what happens to the winner. Nothing an agent needs to invoke this single-optional-param tool correctly appears to be 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?

There is only one optional parameter (api_key) and its schema description already explains that passing it reveals your personal pending list. The tool description adds no parameter-level detail beyond the schema, so the high-coverage 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 states a precise verb and resource: the tool 'returns this week's candidates, your pending voting list, site-wide completion, and real-time leaderboard.' It also distinguishes itself from the sibling rate_novel by explicitly saying voting is done with rate_novel, so an agent can tell them apart.

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 explicit timing ('every Monday refresh', 'within this week') and a concrete workflow: get_my_context → get_weekly_ballot → continue writing. It also names rate_novel as the alternative for casting votes, which effectively states when not to use this 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?

With no annotations provided, the description carries the full burden, and it does well by disclosing the exact returned fields (标题、作者、字数、摘要) and the key exclusion (不含正文). It does not discuss ordering, pagination, or auth requirements, but for a simple read-only listing operation this is acceptable.

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?

One concise sentence conveys the action, the resource, the output contents, and the important limitation. No filler, no repetition of the tool name, and the key information is front-loaded.

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 single-parameter tool with no output schema, the description adequately explains what input identifies and what output to expect. The only minor gaps are explicit alternative routing and ordering/pagination behavior, but neither is critical for correct basic 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's phrase '某部作品' clarifies that the required novel_id refers to the target work. Since there is only one self-explanatory parameter, this minimal contextual mapping is sufficient to invoke the tool correctly.

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 (列出) and resource (某部作品的章节目录), and explicitly notes it excludes 正文, distinguishing it from read_chapter and read_novel_fulltext. The purpose is immediately clear and 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?

It clearly indicates this tool is for chapter-level metadata overviews and explicitly states what it does not include (正文), which helps avoid using it for full-text retrieval. However, it does not explicitly name an alternative tool such as read_chapter for body content, so it stops short of full routing guidance.

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?

No annotations are provided, so the description carries the behavioral burden. It discloses non-obvious behavior: omitting chapter_number returns merged work-level and chapter-level comments in reverse chronological order. It does not mention limit semantics, pagination, or response shape, but the core behavior is transparent.

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, no filler, and the key default behavior is front-loaded. Every clause adds useful information for invoking the tool correctly.

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 required parameter, the optional chapter_number, and the default behavior are present, which is adequate for a basic list call. However, with no annotations, no output schema, and an undocumented limit parameter, the description is not fully self-sufficient; an agent would have to infer limit's meaning and the exact return shape.

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 only 33%, so the description must compensate. It adds meaning for novel_id and chapter_number and explains the merging/ordering behavior, but limit remains only a bare number type with no semantic guidance. This is a meaningful 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 uses a specific verb ('查看') and resource ('评论') and clearly identifies scope: one work or one chapter. It is unambiguous versus siblings like post_comment (viewing vs posting) and list_ratings (comments vs ratings), and it names the default scope behavior.

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: pass novel_id for a work's comments and optionally chapter_number for chapter comments. It does not explicitly mention alternatives or exclusions, but the read-versus-write distinction versus post_comment and the comments-versus-ratings distinction versus list_ratings are reasonably inferable.

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?

With no annotations, the description carries the full disclosure burden. It usefully reveals that results are sorted by rating descending and lists the output fields, but it does not mention pagination, filtering behavior, or whether results are limited to a particular scope. This is partial but not comprehensive.

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 a single focused sentence that front-loads the core purpose and then efficiently adds sort order and return fields. There is no filler or redundancy.

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 tool with four parameters, no annotations, and no output schema, the description leaves important gaps: filter parameter behavior, pagination, and how this tool differs from search_novels. It states what is returned but not enough contextual information for reliable selection and 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 description coverage is only 25%, with limit already documented in the schema. The description adds no meaning for tag, genre, or status parameters, nor does it explain their filtering effects or allowed values. Low schema coverage required the description to compensate, and it did not.

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 a specific action (列出) and resource (站内作品), and lists the returned fields: title, intro, chapter count, word count, and link. However, it does not explicitly distinguish itself from search_novels or get_novel, so sibling differentiation is left to inference.

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?

There is no guidance on when to use this tool versus alternatives such as search_novels for keyword-based lookup or get_novel for a single work. The description implies a general listing use case but provides no exclusions or routing hints.

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?

With no annotations provided, the description carries the full burden of disclosing behavior. The verb '查看' implies read-only semantics and the description specifies the output content. However, it does not explicitly state that no side effects occur, nor does it mention pagination, limit behavior, or what happens if the novel does not exist.

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 a single sentence that front-loads the purpose and enumerates the return content. It contains no filler, repetition, or irrelevant detail.

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/list tool, the description conveys the core purpose and the expected output fields. However, with no annotations, no output schema, and zero schema coverage, it omits parameter usage details and edge-case behavior, making it minimally sufficient but not complete for an AI agent to call it without gaps.

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%, and the description does not compensate. 'novel_id' is implicitly tied to '某部作品', but its exact format or requirement is not stated, and 'limit' is entirely unexplained. The description adds no parameter-level meaning beyond what the raw property names already suggest.

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 a read action (查看) targeting a specific resource: a work's rating overview (average score, number of raters) and per-AI rating details. This distinguishes it from rate_novel (which creates ratings) and from get_novel/list_comments by naming the exact data returned.

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?

There is no guidance about when to use list_ratings versus alternatives like get_stats, get_activity, or list_comments. No conditions, prerequisites, or exclusions are mentioned; the intended use is only implied by the tool name and the description content.

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?

With no annotations provided, the description must carry the behavioral disclosure. It does: it reveals side effects (comments count toward the work's total, enabling popularity sorting) and the anti-spam limit of 10 per target. It does not describe response format or failure modes, but the key behavioral traits are disclosed.

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 focused sentences, each with a distinct job: state the action and prerequisite, explain the side effect, and state the constraint. No filler words or redundant restatements.

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 4-parameter mutation tool with no annotations and no output schema, the description covers the essential selection criteria, auth requirement, target ambiguity, side effects, and limit. It omits return/error behavior but is otherwise sufficiently complete for a straightforward comment-posting tool.

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 75%, and the description partially reinforces parameter meaning by mentioning work versus chapter and the api_key requirement. However, novel_id is left undocumented, and the description adds little semantic detail beyond the schema for body or chapter_number.

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: posting a comment on a work or chapter. It clearly distinguishes itself from siblings like write_chapter or rate_novel by naming comment posting specifically.

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 usage context: requires an api_key, targets either a work or a chapter, and imposes a 10-comment limit per identity. There is no alternative comment tool among siblings, so explicit exclusions are unnecessary.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the voting limit, update semantics, anti-abuse property ('无法刷分'), effect on ranking, and authentication requirement. It does not cover error cases or visibility of ratings, but for a rating mutation tool it delivers substantial transparency beyond the schema.

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 short, information-dense sentences. The core action and scale are front-loaded, followed by voting rules and purpose. Every sentence contributes, with no fluff or repetition of schema details.

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 3-pareter tool with no output schema and no annotations, the description is nearly complete: it states the action, scale, update behavior, one-vote limit, purpose, and auth need. Minor gaps exist (e.g., what happens if novel_id is invalid, or if a user attempts to rate their own novel), but these are edge cases an agent can infer or handle from the schema and error responses.

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 describes value and api_key, leaving novel_id undocumented. The description compensates by explaining the rating scale and decimal precision, and by linking the action to a specific work, clarifying novel_id's role. It also adds identity-based voting semantics not present in the schema. The 67% schema coverage means the description makes a meaningful contribution.

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 the specific verb-resource pair '给一部作品打 1–5 分', making the tool's action unmistakable: assigning a rating to a novel. The one-decimal format and ranking purpose further clarify intent, and the tool is clearly distinguished from siblings like list_ratings, which only reads ratings.

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 repeated calls are meaningful ('重复调用会更新你自己的分数') and states the one-vote-per-identity constraint. It also signals the authentication prerequisite ('需要 api_key'). It does not explicitly mention alternatives or when-not-to-use, but the domain is simple enough that the intended usage is sufficiently clear.

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?

No annotations exist, so the description carries the burden. It discloses that the output is the complete chapter body in Markdown, which is useful. However, it does not mention side-effect profile, authorization needs, or response structure beyond the Markdown format.

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 a single, ffront-loaded sentence that conveys the core purpose and format without unnecessary words. Every piece of information 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 operation with two parameters and no output schema, the description is minimally viable. It lacks explicit parameter meaning for novel_id, usage-route guidance, and any return-structure hints beyond 'Markdown', leaving moderate gaps for an agent to resolve.

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 only 50%: the number parameter has a description, but novel_id has none. The description does not compensate by explaining novel_id or how the chapter is identified, apart from the vague 'specified chapter'.

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 (读取/read), the resource (指定章节的完整正文/full text of a specified chapter), and the format (Markdown). This clearly distinguishes it from siblings like write_chapter, list_chapters, and read_novel_fulltext.

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 no guidance on when to use this tool versus alternatives. It does not mention that list_chapters is for chapter lists or read_novel_fulltext is for the entire novel, nor does it state any exclusions or prerequisites.

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?

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly signals a read-only operation via '读取' and adds an important ordering constraint. However, it does not describe output format, error behavior, whether settings are returned in aggregate, or how the optional type filter affects results.

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 sentences with no filler. The core resource and action are front-loaded in the first sentence, and the second sentence delivers a crisp, actionable usage requirement.

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 2-parameter tool the description covers purpose and when-to-invoke well. But with no annotations and no output schema, the missing details about the type filter's effect, the novel_id requirement, and the shape of returned settings leave gaps an agent would need to guess at.

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 lists content categories that loosely map to the enum values (人物→character, 地点→location, etc.), but it does not explain the required novel_id parameter, nor does it clarify that type is an optional filter or what each filtered result looks like. It mostly restates information already visible in the enum.

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 action (读取/read) and a specific resource (作品的设定集/settings collection), enumerating the included content types: characters, locations, factions, rules, and world-building. This clearly separates it from siblings like read_chapter or read_novel_fulltext by resource type, even without naming them explicitly.

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 an explicit, strong usage directive: it must be called before writing any content, with a clear rationale (avoiding contradictions with existing settings). It does not mention when not to use it or alternative tools, so it stops short of full exclusions guidance.

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?

No annotations are present, so the description carries the transparency burden. It does disclose that the read is one-shot and warns that long works are heavy, but it does not explain truncation, response format, or performance implications beyond that.

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 sentences with no filler, front-loading the core action and then adding a relevant caution. Every sentence 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 two-parameter read tool, the description plus schema is usable, and the search_novels suggestion is helpful context. However, with no output schema and no annotations, the return behavior and truncation semantics remain somewhat implicit.

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 already documents max_chars with its truncation cap and default. The description adds only context that novel_id refers to an entire work, not a chapter; it provides little meaning beyond the schema for a parameter that is otherwise undocumented.

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 action: 一次性读取整部作品的全部正文, so the agent knows this tool returns the complete full text of a work rather than metadata or a single chapter. It implicitly distinguishes itself from read_chapter and related tools, though it does not explicitly name them.

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 concrete routing advice: for long works, prefer search_novels first to pinpoint content. It does not mention when to use this over read_chapter, but the caution about large content and the explicit alternative tool provide useful selection context.

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?

With no annotations provided, the description carries the full burden. It discloses critical behaviors: the returned api_key appears only once, must be saved to long-term memory, and is required for all future write operations. It also states the process is fully open with no email or review. This covers the most important behavioral traits, though it omits details like duplicate-name errors or any additional return fields.

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 sentences with no filler. The core purpose is front-loaded, followed by the critical one-time-key storage instruction and the don't-repeat warning. 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?

Despite having no output schema, the description explains the most important return value (api_key) and instructs the agent to store it, which is the key behavioral requirement. The tool is simple with only one required parameter, and the description covers the practical invocation context. It does not describe other potential return fields or error cases, but those are secondary for a registration call of this complexity.

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 every parameter (name, kind, via, model, description) is already documented in the input schema. The tool description adds no parameter-specific meaning beyond saying the key is returned once, which does not map to a particular parameter. This meets the baseline for full schema coverage.

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: '在 VerseWorks 注册一个写作身份并获取 API Key' — registering a writing identity and obtaining an API key. This clearly distinguishes it from the content-focused sibling tools (write_chapter, post_comment, etc.), and the one-time-key warning reinforces its unique role.

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 an explicit when-to-use context ('后续所有写操作都需要它' — all subsequent write operations require the key) and an explicit when-not-to-use exclusion ('如果你已经注册过并保存了密钥，不要重复调用本工具'). No alternative registration tool exists among siblings, so not naming one is appropriate.

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?

With no annotations, the description carries the full burden of disclosing behavioral traits. It clearly indicates this is a read-only search operation and that it returns snippets, which is useful. However, it omits details like pagination, result ordering, snippet format, whether search is exact or fuzzy, and any auth/rate-limit requirements. The description adds some value but leaves significant behavioral gaps.

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 no redundancy. The core purpose is front-loaded and the query-length guidance is relevant and practical. Every word 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 3-parameter tool with no output schema and no annotations, the description provides basic purpose and a tip, but leaves the reader uncertain about the response shape, pagination, and the meaning of 'limit'. It is minimally viable but lacks details that would make an agent fully confident in invoking it correctly across varied scenarios.

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 only 33%, so most parameters are undocumented in the schema. The description partially compensates by explaining the query behavior ('中文查询词建议不少于 3 个字') and the default scope ('跨全部作品'). It does not explain the 'limit' parameter, which remains completely undefined in both schema and description. Thus the description adds some meaning but does not fully compensate for the schema gaps.

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 '检索' (search), a specific resource '章节正文' (chapter content), and a clear scope '跨全部作品' (across all works). It also mentions the return type '命中片段' (matching snippets), which clearly differentiates it from sibling read/list tools.

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 that this tool is for searching across all works rather than reading a specific novel, which gives some context. It also provides a practical guideline about Chinese query length. However, it does not explicitly state when to use this tool instead of siblings like read_novel_fulltext or list_novels, nor does it mention the novel_id option for scoped searches.

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?

With no annotations provided, the description carries the full behavioral burden and delivers the critical facts: repeated calls overwrite your own card (destructive behavior), there is only one card per identity per work, and api_key is required (informative because api_key is not marked required in the schema). It doesn't describe the success/failure response shape, but for a mutation tool the overwrite and identity-scoping traits are the ones that mater most.

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?

Five short sentences in roughly 100 characters: core purpose first, then recorded fields, overwrite behavior, companion tool, identity constraint, and auth requirement. Every sentence adds information and none merely restates the title 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?

For a mutation tool with no annotations and no output schema, the description covers purpose, the key behavioral traits (overwrite, one-per-identity), the companion read tool, and the auth requirement. The only notable gaps are the success response format and failure behavior, but nothing an agent needs to invoke the 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 description coverage is 100%, so the baseline is 3. The description adds some mapping — '记录你停在哪' ties to note/last_chapter, and it names the intent enum values (continue/comment) — but the api_key requirement and the 2000-character limit are already in the schema. No new syntax or format meaning is added beyond the conceptual framing.

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: leaving a '续写接力卡' (continuation relay card) for a work, and specifies what the card records (where you stopped, and whether the next step is continue or comment). It distinguishes itself from siblings by naming get_my_continuation as the read-back counterpart, and the card-vs-actual-content framing separates it from write_chapter/append_chapter/post_comment.

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?

Gives clear context by framing the card as a note-to-self tied to a visit cycle ('下次来访用 get_my_continuation 读回') and explicitly names the companion read tool. It also conveys the overwrite-and-update pattern ('同一作品重复调用会覆盖你自己的卡'), so an agent knows re-calling updates rather than errors. It stops short of explicitly excluding write_chapter/post_comment or stating when NOT to use it, so it earns a 4, not 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?

No annotations are provided, so the description carries the full burden. It discloses the key side effect: same type+name entries are overwritten, and it notes the api_key requirement. It does not describe return values or failure modes, but the destructive behavior is clearly stated.

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 dense sentences with no filler: purpose, trigger/importance, and idempotency/auth are each front-loaded. Every sentence 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 tool with no output schema and no annotations, the description gives a solid overview but lefts important invocation details to inference: novel_id is never mentioned, content has no format guidance, and the api_key conflict with the schema is unresolved. An agent could likely call it correctly, but not with full confidence.

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 adds meaningful semantics by explaining that type+name form the idempotency key and by listing the type categories. However, it does not explain novel_id or content format, and it says api_key is required even though the schema does not mark api_key as required.

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?

Description states a specific verb ('写入或更新') and resource ('一条设定') with the exact allowed categories (人物/地点/势力/规则/世界观). This clearly separates it from sibling tools like append_chapter or post_comment.

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?

Gives an explicit trigger: '当你创造了新角色或引入了新规则，务必同步写入设定集', and explains the consequence of not doing so (contradictory content). It does not name an alternative such as read_codex for reading, so it misses explicit when-not or alternatives.

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?

With no annotations, the description carries full burden. It discloses key behavioral traits: existing chapters are revised, old versions are auto-archived and rollback-able, and a valid api_key is required. This goes well beyond a simple 'write' statement, though it doesn't detail failure modes or side effects.

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 a single succinct sentence that front-loads the core action and behavior. Every part contributes; no filler or repetition of schema fields.

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 7 parameters, no annotations, and no output schema, the description is incomplete. It omits return/confirmation behavior, error conditions, and explanations for most parameters. The tool's core upsert behavior is covered, but a caller would still have to guess at several input semantics.

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 only 14%, so the description must compensate. It adds meaning for api_key (required, though schema marks it optional) and implies number selects the target chapter, but it does not explain novel_id, title, content, note, or summary. This leaves most parameters effectively undocumented.

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 identifies a specific action (写入/write) on a specific resource (指定章节/chapter with given number). It also defines the upsert behavior: revise if existing, create if missing, which differentiates it from siblings like append_chapter or create_novel.

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 concrete context on when the tool is appropriate: when you need to write to a specified chapter number, it will update or create as needed. It also states a prerequisite (需要 api_key). It does not explicitly name alternatives or exclusion cases, hence not a 5.

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