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

Without annotations, the description carries the burden. It mentions fetching a URL and reporting headers, but does not disclose network behavior, failure handling, rate limits, or authentication needs. It is adequate but lacks depth.

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 efficiently conveys the purpose and scope with no unnecessary words or fluff.

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 sufficiently explains the action and result (reporting headers). However, it could add a note about the return format or failure behavior for completeness.

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 100% coverage with a description for the URL parameter. The tool description reinforces the same concept, adding minimal extra value beyond the schema's example. 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 clearly states the tool fetches a URL and reports on specific security headers (HSTS, CSP, etc.), distinguishing it from sibling audit tools like check_indexability or check_robots.

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 use for security header auditing, but does not explicitly state when to use this tool over siblings or provide exclusions. The context of security headers is clear, but no alternatives are mentioned.

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?

The description clearly indicates a read-only operation (fetch and report) with no destructive behavior mentioned. As there are no annotations, the description adequately conveys the tool's safety profile, though it could note if any side effects 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?

A single sentence effectively conveys the tool's purpose and key details. It is concise and front-loaded, though listing the checked elements separatedly could improve scannability slightly.

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 no output schema, the description does not specify the response format (e.g., boolean or status). It covers the input well but leaves the agent to guess the output structure, which is a gap for tool selection.

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 is fully described in the schema (100% coverage). The description adds no additional guidance beyond the schema's description, resulting in a baseline score.

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 explicitly states the tool fetches a page and reports indexability, using canonical link, meta robots, and X-Robots-Tag. This clearly distinguishes it from siblings like check_headers or check_robots which focus on narrower aspects.

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 guidance on when to use this tool versus siblings. The description implies it's for comprehensive indexability checking, but does not compare to check_headers or check_robots, leaving the agent to infer usage 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 full burden. It states the tool reads robots.txt and reports on AI crawler status and llms.txt existence, which is adequately transparent for a simple read operation. However, it does not disclose behaviors like error handling (e.g., if robots.txt is missing) 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?

The description is a single sentence that conveys the core purpose efficiently, with no fluff. It is front-loaded and every part 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 no output schema, the description clearly explains what the tool reports: which AI crawlers are allowed/blocked and whether llms.txt exists. This is sufficient for an agent to understand the output and invoke the tool correctly.

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

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

Schema coverage is 100% with a single parameter 'url' described as 'The site URL or domain, e.g. example.com'. The tool description adds no additional meaning beyond the schema, so 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 clearly states the verb 'Read' and 'report' and the resource 'robots.txt' and 'llms.txt', specifying exactly what the tool does. It distinguishes itself from sibling tools like check_headers and check_indexability by focusing on robots.txt and AI crawler analysis.

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 usage for checking AI crawler allowances but does not explicitly state when to use this tool versus alternatives like check_headers or dns_snapshot. No exclusions or prerequisites are mentioned, leaving the agent to infer 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?

No annotations are provided, so the description must disclose behavior. It adds value by specifying the method ('DNS-over-HTTPS') and listing all record types. However, it omits important details such as the output format, potential truncation for large zones, error responses, or rate limiting.

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 exceptionally concise: two sentences with no filler. The first sentence front-loads the action and resource, and the second briefly lists use cases. Every sentence contributes value.

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 simplicity of the tool (1 parameter, no output schema, no annotations), the description covers the essential purpose and usage. It could be improved by noting the format of the returned snapshot (e.g., an object with record arrays) or any limitations (e.g., only public DNS). Current coverage is adequate but not exhaustive.

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% (only one parameter, domain, with a description). The description does not add meaningful semantics beyond the schema—it merely repeats the parameter name and gives an example ('example.com'). 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 clearly states the action ('Pull'), the resource ('full DNS snapshot'), and lists the specific record types (A, AAAA, MX, etc.). It is well-differentiated from sibling tools (e.g., check_headers, domain_rating) which cover unrelated aspects like email headers or domain authority.

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 mentions common use cases ('migrations, deliverability, and handoffs'), providing clear context for when to use. However, it does not explicitly state when not to use the tool or mention alternatives, though no direct alternative exists among 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 burden of disclosure. It explicitly states that 'Attribution to Ahrefs is required and returned in the result,' which is a key behavioral trait. It does not mention rate limits or error handling, but for a simple read-only lookup, this is sufficient.

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, front-loading the purpose and range in the first sentence and a critical usage note (attribution) in the second. No unnecessary words 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 one parameter, no output schema, and no annotations, the description covers the essential aspects: what the tool does, the score range, and the attribution requirement. It lacks details on the exact response structure, but for a simple numeric score, it is adequate.

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% for the single parameter 'domain', and the description adds no additional meaning beyond the schema's description ('The domain, e.g. example.com'). Since the schema already provides clear documentation, the description adds marginal 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 clearly states the tool's purpose: 'Look up the Ahrefs Domain Rating (0 to 100 backlink-authority score) for a domain.' It uses a specific verb ('look up') and resource ('domain'), and the score range is provided. The tool is distinct from siblings like check_headers or trace_redirects, as it focuses on backlink authority.

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 usage when needing a domain's backlink authority score, but does not explicitly state when to use this tool versus alternatives. No direct comparison to sibling tools is given, though the purpose is clear enough to infer usage 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?

No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool 'fingerprints' and 'proxies' an external service, implying network dependency and read-only operation, but does not detail rate limits, caching, or potential side effects. Lack of explicit safety traits limits transparency.

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, zero wasted words. The purpose and proxying detail are front-loaded. Every sentence adds value 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?

For a single-parameter tool with no output schema, the description lists all detected elements (CMS, framework, etc.) and additional data (RDAP, Ahrefs Rating). It does not explain the return format or error handling, but the list provides sufficient context for agent selection. A score of 4 reflects minor 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 parameter 'url' has 100% schema coverage with a clear description in the schema. The tool description repeats the same format examples ('stripe.com or https://stripe.com/'), adding no new meaning beyond the schema. 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 clearly states the specific verb 'Fingerprint' and lists the exact resources detected: CMS, framework, hosting, backend, marketing tech, AI website builder, plus domain registration and Ahrefs Domain Rating. This distinguishes it from sibling tools like check_headers or domain_rating which focus on individual aspects.

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 usage for comprehensive tech stack detection, contrasting with siblings that cover individual facets. It mentions proxying lilStack service but does not explicitly state when to use versus alternatives or provide exclusions. The context of sibling tools helps clarify its broad scope.

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?

Discloses that it follows every redirect hop and reports status codes and Locations. No destructive behavior implied. No annotations provided, but description covers core behavior effectively.

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?

Single sentence that efficiently conveys the tool's action and output. Could be slightly more terse but is well-structured and focused.

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 tool with one parameter and no output schema, the description provides enough context to understand the tool's function and output. Lacks details on limits (e.g., max hops), but sufficient for typical use.

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 a clear parameter description. The tool description essentially restates the schema's purpose, adding no extra semantic detail beyond what the schema provides.

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?

Clearly states the tool follows all redirect hops and reports status code and Location for each hop. Distinguished from siblings like check_headers as it focuses on redirect chain tracing.

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?

Describes the purpose (spot broken/sneaky chains) but does not explicitly state when to use versus alternatives like check_headers or dns_snapshot. No clear 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?

The description discloses the main actions (fetch, extract, validate, list) and their order. With no annotations provided, it carries the full burden and does a fair job, though it omits details like error handling or response 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?

One concise sentence that front-loads the main purpose. Every word contributes to describing the tool's action, with no 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?

For a simple tool with one parameter and no output schema, the description covers the essential workflow. It could optionally mention what the output contains (e.g., types and validation errors), but it is still sufficiently complete for an agent to understand the tool's purpose.

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 'url' is already well-described in the schema (e.g., 'The page URL, e.g. https://example.com/'). The description adds no additional semantics beyond what the schema provides, so the baseline score 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 clearly states the verb (fetch, extract, validate, list) and the specific resource (JSON-LD structured-data blocks). It distinctly sets this tool apart from sibling tools like check_headers or check_robots, which address different aspects of a page.

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 guidance on when to use this tool versus alternatives. While the sibling tools suggest different use cases, the description does not provide when/when-not or preference hints, leaving the agent to infer context.

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