Web Validator by DigestSEO

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds meaningful behavioral details beyond annotations: it respects robots.txt, fetches at most 8 same-origin pages, does not authenticate/execute JavaScript/fetch assets, and explains pagination with page_offset. This fully informs the agent of runtime 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 compact and front-loaded, with four purposeful sentences covering purpose, constraints, exclusions, and continuation. No redundant or filler content; 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?

With a high-quality schema, rich annotations, and an output schema present, the description still adds necessary context: the sitemap-first approach, robots.txt compliance, the 8-page cap, deduplication, non-authentication, and pagination. This makes the tool fully understandable and usable without further clarification.

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 baseline is 3. The description adds value by explaining page_offset's role in continuing the audit ('Use page_offset to continue when more sitemap pages remain') and implicitly clarifies max_pages bound via 'at most 8 same-origin HTML pages per call'. This enriches parameter understanding 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 clearly states the tool performs a bounded, sitemap-first audit of a public website. It specifies the resource (public website), the scope (sitemap-first, at most 8 pages), and the deliverables (compact page summaries plus deduplicated findings), distinguishing it from sibling tools like check_broken_links and audit_public_webpage.

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 use the tool ('for a bounded, sitemap-first audit of a public website the user owns or is authorized to inspect') and clearly lists what it does not do (e.g., site-wide link checks), which implies when not to use it. However, it does not explicitly name alternative sibling tools, so the guidance is strong but not fully explicit.

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

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

Beyond the readOnly/idempotent/openWorld annotations, the description adds substantial behavioral context: it fetches only one bounded static HTML response, checks links only when requested, and explicitly states it does not crawl, execute JS, authenticate, or fetch linked stylesheets, plus an authorization requirement. This fully discloses side effects and limitations.

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

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

Three sentences, front-loaded with the trigger condition. Every sentence conveys distinct, necessary information without redundancy or 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?

The tool has an output schema, so return format is covered. The description explains what checks are performed, linked-checking behavior, limitations, and authorization requirements, making it complete for a tool 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 parameters are already well-documented. The description adds slight context by mentioning 'one live public webpage URL' and 'checks links only when requested,' but does not meaningfully supplement the schema's parameter descriptions.

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

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

The description clearly states the tool audits a single public webpage by fetching HTML and running validation/SEO/JSON-LD checks. It explicitly contrasts with site-level and link-checking tools by noting it checks links only when requested and does not crawl.

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 opens with 'Use this when the user provides one live public webpage URL,' giving explicit when-to-use guidance. It lists exclusions (no crawl, no JavaScript) that imply when-not-to-use, but does not name specific alternative tools; the alternatives are only available as sibling names.

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

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

Annotations already indicate a safe, read-only, idempotent operation, so the safety profile is covered. The description adds key behavioral context by explicitly stating it does not fetch a live webpage, clarifying the offline nature of the operation. This goes beyond annotations but isn't exhaustive; still, it effectively sets expectations.

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: the first states the purpose, the second clarifies a critical exclusion and provides an alternative. It is front-loaded, concise, and 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?

With one parameter, a descriptive schema, comprehensive annotations, and an output schema, the description fully equips an agent to select and invoke the tool correctly. It explains the input type, the scope, and the alternative, making it complete for this 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?

Schema coverage is 100%, with the 'html' parameter described as 'Raw HTML markup supplied by the user; this is not a webpage URL.' The description reinforces this by saying 'supplied HTML' and 'does not fetch a live webpage,' but it adds no new information beyond the schema. Baseline 3 applies.

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

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

The description clearly states the tool performs 'a focused SEO/accessibility-signal check of supplied HTML' with a specific verb ('audit') and resource ('SEO metadata'). It explicitly distinguishes itself from sibling 'audit_public_webpage' by noting this tool does not fetch live webpages, providing clear differentiation.

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 guidance is explicit: use this when you have raw HTML to check, and use 'audit_public_webpage' when you have a URL. This directly addresses when to use vs. alternatives, leaving no ambiguity.

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds concrete behavioral constraints: it does not fetch base_url, follow redirects, or retain response bodies. This goes beyond the annotations and clarifies important side-effect boundaries.

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 is front-loaded with the purpose ('Use this for...') and includes the key scope and limitations. No filler 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?

The description, combined with rich annotations and a full output schema, covers the tool's purpose, limits, and exclusions. It is slightly incomplete on when to prefer this over sibling tools, but that is a usage-guidelines gap rather than a contextual gap.

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

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

Schema description coverage is 100%, so the schema fully documents html, base_url, and max_links. The description adds minimal extra value by restating behavior around base_url and the 20-link limit, but it does not introduce new parameter semantics 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 clearly states the tool performs a focused check of up to 20 public HTTP(S) links found in supplied HTML. The verb 'check' and the specific resource (public links) distinguish it from sibling tools like audit_public_site or validate_html.

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 'Use this for a focused check...' giving clear context for when to use the tool. It also mentions what it does not do (fetch base_url, follow redirects, retain response bodies), which helps rule out inappropriate use, though it does not name alternative 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?

Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior. The description adds valuable context beyond annotations: it does not fetch the base_url, and it performs a combination of validation checks rather than a single check. It could additionally clarify that linked stylesheets are not fetched, but that is covered in 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?

Two sentences, front-loaded with use-case, explicit alternative, and no filler. Every sentence contributes actionable guidance without 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?

Given the output schema, complete annotations, and full parameter descriptions, the description covers the essential decision context: when to use, what it combines, and what it does not do. It also names a sibling for live URLs. The combination of structured data and concise description is sufficient for correct tool 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 100%, so baseline is 3. The description adds meaningful context such as 'local CSS' clarifying the css parameter and explicitly stating base_url does not fetch the webpage, which reinforces the schema's parameter descriptions. This lifts it slightly above baseline.

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

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

The description uses a specific verb ('Generate') and clearly identifies the resource ('validation report') and scope ('HTML markup already supplied'). It differentiates from siblings by explicitly naming the combined checks (Nu HTML, local CSS, SEO, accessibility-signal, JSON-LD) and contrasting with audit_public_webpage.

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 states when to use ('Use this when HTML markup is already supplied') and what not to use it for ('does not fetch a webpage'), naming the alternative tool ('use audit_public_webpage for a live URL'). This gives clear decision-making 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?

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description adds key behavioral context: parsing occurs locally 'inside the Worker' and 'without contacting an external service', which informs the agent about execution boundaries and privacy implications. This is valuable context not covered by 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 two sentences, front-loaded with a usage directive, and every word serves a purpose. It avoids redundancy with schema or annotations while providing necessary context.

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 single-parameter tool with a rich output schema and comprehensive annotations, the description covers the essential aspects: what it does, when to use it, and a key distinguishing behavior (no external service). No critical information is missing.

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

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

The schema already provides 100% parameter coverage with a clear description ('Raw CSS source supplied by the user'). The tool description merely repeats 'raw CSS source' without adding further semantics about format, constraints, or edge cases, so it adds no extra value 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 clearly states the tool parses CSS source and returns syntax messages, using a specific verb and resource ('parses it', 'CSS'), which distinguishes it from sibling validation tools like validate_html and validate_schema_markup. The mention of 'without contacting an external service' further clarifies its unique scope.

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 states when to use it ('Use this when raw CSS source is supplied'), providing a clear user scenario. It does not explicitly mention alternatives or when not to use it, but the condition is sufficient for basic 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?

Annotations already cover read-only/idempotent/non-destructive. The description adds context about invoking an external service (validator.nu), implying network dependency, and clarifies the return type (validation messages). This provides meaningful behavioral context beyond annotations.

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

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

Two sentences, front-loaded with the usage condition, and no unnecessary words. It is optimally concise while conveying all essential information.

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 an output schema, the description covers purpose, input type, external service, return value, and sibling differentiation. There are no critical 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 schema covers the only parameter 'html' with 100% description coverage, including 'Raw HTML markup supplied by the user; this is not a webpage URL.' The tool description adds no additional parameter semantics beyond what the schema already provides, so baseline 3 applies.

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

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

The description clearly states the tool sends raw HTML markup to the Nu HTML Checker and returns validation messages. It also distinguishes itself from the sibling 'audit_public_webpage' by noting that tool is for live URLs.

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?

Explicitly provides when to use: 'Use this when raw HTML markup is supplied.' It also names an alternative: 'For a live URL, use audit_public_webpage.' This is clear, actionable 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?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds useful context beyond annotations by specifying it 'parses JSON only' and does not perform semantic validation, which informs the agent of the tool's limited scope and prevents misuse.

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 extremely concise, consisting of two sentences that deliver all necessary information upfront. Every word earns its place, with 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?

Given the presence of an output schema, rich annotations (read-only, idempotent, non-destructive), and a single well-documented parameter, the description fully covers the tool's behavior and limitations. It is complete for the tool's simplicity and the structured data surrounding 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?

The input schema already covers the single parameter with a clear description ('Raw HTML markup supplied by the user; this is not a webpage URL.'). The tool description reinforces 'supplied HTML' but adds no additional parameter meaning, 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?

The description clearly states a specific action: 'focused JSON-LD syntax check of supplied HTML'. It distinguishes itself from other validation tools by explicitly noting it does not validate Schema.org vocabulary semantics, which sets it apart from broader audit 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 opens with 'Use this for a focused JSON-LD syntax check', providing explicit when-to-use context. It also gives a clear exclusion: 'does not validate Schema.org vocabulary semantics', which tells the agent when NOT to use it, though it does not mention alternative tools by name.

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