llms.txt 檢查工具

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 operation type (read-only inspection of robots.txt) and the shape of the result (allowed vs blocked per crawler), but says nothing about behavior when robots.txt is missing, network/permission requirements, or rate limits.

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 names the mechanism, the resource, and the concrete result set with no filler. 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?

With no annotations and no output schema, the description does a fair job covering what is checked and what is returned (13 crawlers, allow/block), but leaves the url format and failure behavior undocumented and does not differentiate itself from the three llms.txt siblings.

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?

One parameter (url) with 0% schema description coverage. The description references 'the website robots.txt', hinting the parameter is a site URL, but it does not specify whether a full URL with scheme, a domain, or a path is expected – a meaningful gap the description should have filled.

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 and resource (reads robots.txt) and specifies the output (allow/block status for 13 named AI crawlers). It contrasts implicitly with the llms.txt siblings by targeting robots.txt, though it never names them directly.

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 inferable from the purpose – an agent would call this to determine whether AI crawlers may access a site – but there is no explicit when-to-use, when-not-to-use, or routing guidance relative to check_llms_txt or validate_llms_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?

No annotations are provided, so the description carries the full burden, and it does disclose the output shape (0–100 score plus improvement suggestions) and the breadth of checks performed, which is genuinely useful. It omits operational traits such as that it performs live network fetches, any rate/timeout behavior, and whether it is purely read-only — gaps that matter for a network-invoking audit tool.

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 purpose front-loaded and the return value appended at the end. Every listed item earns its place as a scope declaration, though the enumeration is packed tightly enough to read as a list rather than prose.

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 usefully compensates by naming the return value (0–100 score and improvement suggestions) and the full scope of inspected artifacts. It is sufficiently complete for an agent to decide and call, with the only real gap being the missing sibling differentiation.

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?

Only one parameter exists and the schema description coverage is 100% ("網站網址或網域"), so the schema already documents the input. The description adds nothing about expected URL format, whether scheme is optional, or how bare domains are handled, so the baseline of 3 applies.

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

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

States a specific verb (檢查/check) and resource (a site's AI-friendliness), and enumerates the concrete artifacts inspected: llms.txt existence/format, llms-full.txt, Markdown pages, robots.txt per AI crawler, and sitemap. However, it never differentiates itself from siblings that cover overlapping ground (validate_llms_txt for llms.txt format, check_ai_crawler_access for crawler rules), so an agent cannot tell apart the composite audit from the narrower validators.

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, when-not, or alternative guidance is present despite three closely related siblings. An agent must infer from the field list that this is the broad umbrella check rather than the targeted validate_llms_txt or check_ai_crawler_access call.

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 behavioral burden. It usefully discloses that the tool fetches external resources (homepage and sitemap) and that the result is a draft rather than a finished file, but it says nothing about network/access requirements, whether it writes or returns the artifact, or failure 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?

A single compact sentence that front-loads the action and the inputs; nothing is wasted. Brevity is appropriate, though the small budget is spent on purpose rather than on the missing operational 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 one-parameter tool with no output schema, the description covers what it does but leaves the agent unsure what the draft result is (returned text vs generated file) and whether the target site must be publicly reachable. Adequate but with 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?

The single 'url' parameter has 0% schema description coverage, so the description must compensate. It implies the url is the site whose homepage and sitemap will be read, which adds minimal meaning, but gives no format, scope, or example (root URL vs full page URL).

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 ('自動產生 llms.txt 草稿') plus the mechanism ('讀取網站首頁與 sitemap'). This distinguishes it from the sibling tools check_llms_txt and validate_llms_txt, which check/validate rather than generate, though the distinction is implied 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?

There is no explicit guidance on when to use this tool versus check_llms_txt, check_ai_crawler_access, or validate_llms_txt. The generation intent is inferable from the verb, but no conditions, prerequisites, or exclusions are given.

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 falls short. It does not say whether validation is strict or lenient, whether it merely reports issues or normalizes content, how results are returned, or whether any rules beyond the four listed are enforced. For a validator with no output schema this is a meaningful 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 front-loaded sentence with no filler, where every clause identifies a validation rule. It is efficient, though its terseness contributes to the missing usage and output guidance.

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

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

The description lists the criteria being validated, which is the most important context for a format checker, but with no output schema and no annotations, it should also indicate what a result looks like and how it differs from check_llms_txt. It is adequate but incomplete for the tool's 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?

The single parameter 'content' has no schema description (0% coverage), but its name plus the description's 'a piece of llms.txt content' make the meaning clear enough. The description adds only marginal detail over the parameter name itself, so a baseline 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 specific verb and resource (validate llms.txt content) and enumerates the checked rules (H1, summary, sections, links), which is concrete. It never names its closest sibling check_llms_txt, so an agent cannot tell from the text alone why it should validate supplied content rather than check an existing file.

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 explicit when-to-use or when-not-to-use guidance, and no mention of alternatives. The phrase '一段 llms.txt 內容' faintly implies the caller supplies the text (as opposed to check_llms_txt operating on a deployed file), but this distinction is left for the agent to infer against a very similarly named sibling.

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