搜尋引擎與 AI 爬蟲工具箱

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

No annotations are provided, so the description must carry the full behavioral burden. It describes the data returned but does not state whether the operation is read-only, whether it requires authentication, whether it has side effects, or how the list is sourced/updated. For a tool with zero annotation coverage, this is a significant gap.

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, dense sentence that efficiently communicates the tool's data content without filler. It is front-loaded with the resource name and then lists key attributes.

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 absence of annotations, output schema, and schema descriptions, the description should do more to explain the sole enum parameter and usage context. It partially covers return data but leaves key gaps for an agent to invoke 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 schema has one enum parameter ('type') with five values and 0% description coverage, but the description never mentions the parameter or explains what the enum values mean. The description does not compensate for the missing schema documentation.

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 is a noun phrase that names the resource ('AI 與搜尋引擎爬蟲 User-Agent 名單') and enumerates the fields it includes (業者、用途、robots.txt 合規、官方 IP 清單). It clearly states the tool's output content but does not distinguish it from siblings like check_site_crawlers or verify_crawler_ip.

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 when-to-use or when-not-to-use guidance. The description only lists the contents; it does not mention alternatives or the context in which an agent should call this tool instead of 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?

No annotations are provided, so the description carries the full disclosure burden. It implies a pure computation (URL construction) but never states that it has no side effects, what the return shape is (one URL per engine, an array, an object?), or whether unsupported operators for engines like Baidu are dropped or error out.

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 front-loaded sentence with the purpose first and the parameter inventory in a compact parenthetical. Nothing is wasted and no preamble is present.

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 six parameters, no annotations and no output schema, the definition covers intent and parameter meaning but omits the return format and engine-specific operator caveats. Adequate to call the tool, but an agent still has to guess what it receives back.

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 compensate, and it largely does: 關鍵字→q, 完全符合→exact, 排除字→exclude, 限定網站→site, 檔案類型→filetype, 時間→time. This maps all six parameters to their meaning. It stops short of 5 because it gives no format hints (multiple values, delimiters) beyond what the enum already supplies.

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 (產生 / generate) and resource (進階搜尋網址 / advanced search URLs) and names the five target engines, so the agent knows exactly what comes out. It does not explicitly distinguish itself from the sibling search_operators, which is a plausible point of confusion, keeping it below 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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives such as search_operators. The agent must infer that this is the tool for producing ready-to-click URLs rather than explaining syntax.

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 supplied, so the description carries the full disclosure burden. It reveals that output includes improvement suggestions, which is useful, but says nothing about whether this issues live network requests, required permissions, rate limits, or whether the operation is safe/read-only. Roughly the same gap flagged in the mid-tier update_drive example.

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 front-loaded sentence that lists the checked artifacts efficiently with no wasted words. Compact and scannable, though the dense enumeration slightly reduces readability.

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

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

With no output schema and no annotations, the description covers the inspection scope and the existence of recommendations, which is adequate for a one-parameter audit tool. However, it omits the parameter's expected format and any routing versus the similar test_robots_txt sibling, leaving clear 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 never elaborates on the single 'url' parameter. It implies a website is targeted but provides no format, required scheme, example, or scope (root domain vs full page). With zero schema coverage the description should compensate, and it does 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?

States a specific verb (檢查) and enumerates the exact resources inspected (robots.txt, Sitemap, llms.txt) plus the crawler bots whose blocked status is checked, ending with a concrete deliverable (改善建議). This is far more specific than a tautology. It falls short of 5 only because it never names or contrasts the overlapping sibling test_robots_txt.

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 when-to-use, when-not-to-use, prerequisites, or alternatives. Sibling tools like test_robots_txt, verify_crawler_ip and ai_crawler_list clearly overlap, yet the agent gets no signal about which to pick. Usage must be inferred entirely from the name and scope list.

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 behavioural burden, and it delivers almost nothing: it never says whether the output is an HTML snippet, a JSON object, or plain text, nor whether the tool fetches the URL, validates anything, or is purely a string builder. That last point matters a lot because a 'url' parameter could imply network access.

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 tight sentence that front-loads the verb and enumerates outputs without padding. Marks come off only because the brevity is achieved partly by omitting information the agent needs rather than by trimming 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?

With no annotations, no output schema, and 0% parameter documentation, this definition leaves the agent guessing about return shape, the meaning of six parameters, and whether generation has side effects. For a 6-parameter generator it is materially under-specified.

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% across 6 parameters, so the description must compensate and does not. Worse, it claims to emit 'canonical' output although no canonical input parameter exists, and it never explains how 'type' (the one enum), 'image', or 'site_name' shape the generated tags.

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 pair ('產生' + the list of tag families: title, description, canonical, Open Graph, Twitter cards, JSON-LD), so an agent can tell this is a meta-tag generator rather than a validator or preview tool. It does not, however, differentiate itself from near siblings such as serp_preview or generate_robots_txt, so it stops 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?

There is no when-to-use guidance, no prerequisite (e.g. that a title is mandatory), and no mention of alternative tools for related tasks. The agent must infer context entirely from the name.

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 never states whether the tool writes the file to the site, returns text for the user to deploy, or requires write/auth permissions. The gloss on block_training ('封鎖 AI 訓練保留搜尋') is useful, but the core behavioral question of what actually happens on invocation is unanswered.

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 dense sentence with the resource named first and the options enumerated after; every clause carries information. It is compact if slightly crowded — the disallow/Sitemap note arrives without a separator from the mode list.

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 annotations, no output schema, and 0% schema description coverage, the description is too thin. It omits deployment/return behavior, permission needs, and the relationship to the sibling test_robots_txt, all of which an agent needs to invoke this 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 0%, so the description must compensate, and it partially does: it maps the mode enum values and names the disallow-path and Sitemap inputs. However, only block_training is explained among the four modes, and no format is given for sitemap (URL?) or disallow (comma-separated list?), leaving the other three parameters ambiguous.

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 (產生/generate) and resource (robots.txt), and enumerates the four modes so the agent understands the output space. It does not explicitly distinguish itself from the sibling test_robots_txt, which is the natural adjacent tool.

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 when-to-use or when-not-to-use guidance is given. The mode names imply intent (e.g. block_training for blocking AI training while keeping search), but the description never says to use test_robots_txt to validate the result or when generation is appropriate versus auditing existing rules.

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 does disclose what is inspected and what is returned (a score and a prioritized fix list), implying a read-only assessment, but it says nothing about how the page is fetched, whether authentication is needed, or any rate/latency constraints.

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?

Purpose and output are front-loaded, and the dense parenthetical check-list packs meaningful specifics into one sentence without filler. The list is long but every item earns its place as audit criteria.

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

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

With no annotations and no output schema, the description must justify the tool on its own. It adequately conveys scope and return content, but omits operational context (live fetch vs. cached, auth, failure modes) that an agent would want before invoking it.

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% for the single url parameter, so the description should compensate. Context makes clear the parameter is the page to audit, but no format, protocol, or required/optional detail is added beyond the implicit 'webpage' reading.

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 (a webpage) and a specific action (auditing AEO/GEO citation readiness), then enumerates the exact signals it checks (crawler readability, answer-first opening, question headings, tables, JSON-LD citation, llms.txt, etc.) and the output (score plus prioritized fix list). This clearly separates it from generic crawler/robots siblings, though it never names an alternative tool.

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?

Usage is implied by the enumerated checks, so an agent can infer when it applies, but there is no explicit when-to-use, when-not-to-use, or comparison against neighbors like check_site_crawlers or test_robots_txt.

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 supplied, the description carries the burden and delivers meaningful operational context: the site-side key-file requirement and the 100-URL cap. It still omits error behavior (e.g. what happens on invalid key or over-limit) and whether results are per-URL, but the disclosed constraints are genuinely 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?

A single sentence that front-loads the core action and appends the prerequisite and limit in parentheses with no wasted words. Appropriately sized for the tool's simplicity.

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 premise and quota are covered, but with 0% schema coverage and no annotations, host and keyLocation remain entirely unexplained, and there is no output schema to describe per-URL submission results. Adequate but with clear gaps for a 4-parameter 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 0% across 4 parameters, so the description must compensate. It implies the urls array is capped at 100 and that key relates to a hosted key file, but says nothing about host or the keyLocation parameter, leaving half the parameters undocumented in both places.

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?

Names a specific action (notify search engines via IndexNow), the affected resource (updated URLs), and the concrete engines (Bing, Yandex, Naver). This is clearly distinguishable from siblings like search_submit_guide (a guide) or build_search_urls (a URL builder).

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?

States the key prerequisite (a key file must already be hosted on the site) and the constraint (max 100 URLs), which tells the agent when this call is valid. It does not, however, contrast itself with sibling tools or explain failure conditions, so it stops short of full when/when-not 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 full behavioral burden. It discloses the data source (StatCounter) and temporal scope (latest month), which are key traits beyond schema. It does not mention rate limits, caching, or methodology caveats, but for a simple read-only lookup this is reasonably complete.

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, front-loaded sentence that packs the resource, regions, example engines, data source, and recency with zero waste.

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 zero parameters, no annotations, and no output schema, the description adequately covers source, scope, and freshness. It does not describe the return format (e.g., list of engines with percentages), but for a simple market-share lookup this is a minor gap.

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

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

The tool has zero parameters, so the baseline is 4. The description adds no parameter information, which is appropriate given there are none.

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 specifies the resource (search engine market share), scope (Taiwan and global), data source (StatCounter), and freshness (latest month). It lacks an explicit verb but is unambiguous about what the tool returns. Sibling tools are unrelated, so no differentiation is needed.

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 explicit when-to-use guidance or alternatives are stated. However, the purpose is self-evident for a data lookup tool, and there are no similar siblings, so implied usage is sufficient. It does not say when not to use 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?

No annotations are provided, so the description carries the full burden. It does disclose content scope (support matrix plus deprecated operators), which is useful behavioral context, but says nothing about whether the call is read-only, its cost/rate profile, or how results are shaped.

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 compact sentence with the subject (operators) and the covered engines front-loaded, followed by the extra scope of deprecated operators. No filler, though the parenthetical example list is slightly dense.

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, no-output-schema lookup tool the description is adequate about what is compared, but it omits how the optional engine parameter filters the result and what an unfiltered call returns, leaving a meaningful gap.

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

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

Schema description coverage is 0% for the single 'engine' parameter, so the description must compensate. It does name all four engines in prose, matching the enum, but adds no format or semantics beyond that — notably it never explains that engine is optional or what an omitted engine returns.

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 — search operator support across Google, Bing, Yahoo and Baidu, plus deprecated operators — and lists concrete examples (site:, filetype:, intitle:, before:). It is distinguishable from siblings like build_search_urls or serp_preview, though the verb is implied (a lookup/reference) rather than stated.

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?

Usage is only implied: an agent can infer you call this when you need to know whether an operator works on a given engine. There is no explicit when-to-use statement, no mention of alternatives such as build_search_urls for constructing queries, and no exclusions.

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 behavioral burden, and it discloses almost nothing: it does not say whether the tool is read-only, whether it returns static documentation versus live data, whether it costs quota, or what happens on invocation. The phrase 'tools, steps and API' hints at informational output but is too vague to rely on.

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 string is short, but brevity here stems from under-specification rather than economy. It is a topical fragment with no front-loaded verb or outcome, so the one sentence present does not earn its place as a functional description.

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

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

With no annotations, no output schema, and no parameters, the description is the only source of information available to the agent, and it provides essentially none about returns, behavior, or correct timing. For a zero-arg tool this could be adequate if it stated what the call yields, but it does not.

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 declares zero parameters, so there is nothing for the description to clarify or compensate for. Baseline of 4 applies since no parameter semantics are at stake.

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 text reads like an article or blog headline ('tools, steps and API') rather than a statement of what the tool does. It gestures at a topic (getting sites indexed by search engines) but never says whether the tool returns a guide, performs a submission, or generates recommendations. It also fails to distinguish itself from siblings like indexnow_submit or generate_robots_txt that occupy the same indexing space.

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 when-to-use guidance, no prerequisites, and no named alternative. An agent cannot infer from the text whether this should be called before, instead of, or alongside indexnow_submit or check_site_crawlers.

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, but 'preview' and 'estimate' do convey a non-mutating, computational operation. It does not state it is read-only, whether the url parameter affects rendering, or what the response contains, leaving real gaps for a tool with zero annotation coverage.

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

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

A single front-loaded sentence that wastes no words and puts the action and the outputs first. It is efficient, though it packs two distinct ideas (preview and pixel estimation) without separating them.

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 three-parameter, no-output-schema utility this is close to adequate, but with no annotations and 0% schema coverage the description should explain the url parameter and the shape of the preview result. It is the minimum viable level for 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 0%, so the description must compensate and largely fails to. It references 標題 and 描述 conceptually, matching two of three params, but omits the url parameter entirely and adds no format, length, or default 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 gives a specific verb (預覽/preview) and resource (Google 搜尋結果標題與描述) plus the computed outputs (中文像素寬度, 截斷位置). This is far more concrete than a generic 'SERP tool' and clearly distinct from siblings like generate_meta_tags. It stops short of explicitly naming which sibling it replaces.

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 statement of when to use this versus alternatives such as generate_meta_tags or check_site_crawlers, and no prerequisites or exclusions. Usage is only inferable from the purpose (drafting/validating titles).

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 full behavioral burden. It references RFC 9309 but does not disclose whether the test performs network fetches, whether it is read-only, what result format to expect, or any rate-limit or permission constraints.

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 front-loaded sentence with no filler. It is appropriately sized for a terse tool summary.

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

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

With no annotations, no output schema, and 0% parameter description coverage, the description is too thin. It does not explain the required robots parameter, the expected output, or usage context for a three-parameter evaluation 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 0% with three parameters. The description names User-Agent and path conceptually, but it omits the required 'robots' parameter entirely and provides no format or meaning for any parameter, failing to compensate for the empty 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?

States a specific verb ('test') and resource (whether a User-Agent can crawl a path) under RFC 9309, so the core action is clear. It does not, however, distinguish this tool from siblings like generate_robots_txt or check_site_crawlers.

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 only the tool's purpose, with no explicit when-to-use guidance, prerequisites, or alternatives. It never says when to prefer this over generate_robots_txt, check_site_crawlers, or other robots-related 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?

With no annotations and no output schema, the description carries the full behavioral burden. It does disclose the verification method (official IP list plus reverse DNS lookup), which is genuinely useful context, but says nothing about the result shape, failure behavior, or rate/latency characteristics of the DNS lookup.

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 action and the covered bot set, with the method tucked into parentheses. No filler and nothing redundant.

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?

A verification tool with two parameters, no annotations, and no output schema needs to explain what comes back (boolean, matched bot name, or error) and how the optional 'bot' parameter narrows the check. The method disclosure partially compensates, but the return contract and parameter roles remain unaddressed.

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 neither parameter is explained in the description. The listed bot names hint at valid values for the optional 'bot' parameter but no enum exists to confirm, and the required 'ip' format (IPv4/IPv6) and the role of 'bot' (restrict check to one bot vs auto-detect) are left 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?

States a specific verb (verify) and resource (crawler IP) and names the concrete bot families covered (Googlebot, bingbot, GPTBot, ClaudeBot, etc.). It is distinguishable from siblings like ai_crawler_list or check_site_crawlers, which are about listing crawlers or checking site visits rather than validating a single IP.

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 use case (anti-spoofing / confirming a claim that an IP belongs to a named bot) but never states when to pick this tool over check_site_crawlers or ai_crawler_list, and gives no prerequisites or exclusions. An agent must infer the intended trigger entirely.

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 behavioral burden. It never says whether the tool only reads/validates or mutates, what a result looks like, error handling, or whether auth/API access is needed. '檢查' weakly implies a read-only lint, but that is inference, not 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?

A single dense sentence with the check rules parenthetically front-loaded; no filler. It is efficient, though cramming chapters plus four metadata limits into one clause slightly blurs the structure.

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?

Five undocumented parameters, no annotations, and no output schema. The description supplies chapter validation thresholds but leaves input formats, the duration parameter, and the shape of the returned findings unspecified — insufficient for an agent to call the tool reliably.

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% across 5 parameters, so the description must compensate. It usefully encodes chapter rules (must start at 0:00, at least 3 segments, 10 seconds each) and mentions title/description/tags/hashtag limits, but never specifies the expected input format (e.g., multiline text vs. array) and entirely omits the 'duration' parameter and the concrete numeric limits.

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 (檢查/check) and resource (YouTube 章節時間碼 plus 標題、說明、標籤、主題標籤), giving a clear picture of what is validated. No sibling tool touches YouTube, so differentiation is moot; it only lacks explicit scope boundaries for the check.

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?

Usage is implied — validate a video's chapters and metadata length limits, presumably before publishing. There is no explicit when-to-use statement, no prerequisites, and no named alternative, so the agent must infer the context.

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