llms-txt-validator

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

With no annotations, the description carries the full behavioral burden, and it only partially discharges it. It discloses the data sources (homepage, sitemap) but not whether the result is written to disk or returned inline, what happens if the site lacks a sitemap, or any network/permission requirements.

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 tightly written sentence, front-loaded with the action and resource. Every clause earns its place; nothing is padded or repeated from the schema.

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

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

There is no output schema and no annotations, so the description must cover the return contract, and it does not say whether a file is produced or content is returned. The input side is fully covered, leaving a meaningful gap on the output side.

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% with a single well-documented 'url' parameter, so the schema does the heavy lifting and baseline 3 applies. The description adds only that the URL is treated as a domain to scaffold from, not new format or constraint information.

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 (scaffold) and resource (starter llms.txt) with its data sources (homepage and sitemap). The generate-vs-validate split is obvious against validate_llms_txt and validate_llms_txt_content, though the description never names those siblings explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use, when-not-to-use, or alternative routing. The verb 'scaffold' weakly implies 'use me to create a new file first', but nothing tells the agent whether an existing llms.txt should be validated instead, or whether the domain must already have a sitemap.

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 usefully discloses the return payload (score, findings, AI-discoverability signals), which compensates for the absent output schema, but says nothing about read-only/non-destructive nature, network fetching, failure modes, 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?

Two compact sentences with the purpose and return value front-loaded and no filler. Slightly terse given the ambiguity with siblings, but every sentence earns its place.

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

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

For a single-parameter read tool with no output schema, the description covers both the action and the return values, which is close to sufficient. The remaining gap is the lack of differentiation rules against its two 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?

Only one parameter with 100% schema description coverage, which already documents the domain/URL format and the path variant. The description's "Pass a domain or URL" merely echoes the schema, adding no new syntax or edge-case detail, 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 and resource (validate a website's llms.txt) and adds the output shape (0-100 score, findings, discoverability signals). It differentiates implicitly from generate_llms_txt (creation vs validation) and validate_llms_txt_content (live website vs raw content), but never names them explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a minimal input instruction ("Pass a domain or URL") but no when-to-use framing, no prerequisites, and no guidance on choosing between this and validate_llms_txt_content or generate_llms_txt. The choice between validating a live site versus raw content is left entirely to inference.

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 usefully discloses that the tool performs no fetch and returns the same score and findings as its counterpart, but says nothing about determinism, side effects, permissions, or the shape of the findings.

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

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

Two short sentences with the key differentiator front-loaded. No filler or redundancy; every clause carries 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, read-only validation tool with no output schema, the description covers what the tool does, its key constraint (no fetching), and roughly what it returns. Only the structure of the returned findings is left unstated, which 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?

Schema coverage is 100% and there is a single required parameter, so the schema already defines 'content' fully. The description reinforces that the input is text already held locally, but adds no format or constraint detail beyond the schema's own Markdown description.

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 (validate raw llms.txt content) and distinguishes itself from the fetching sibling via 'text you already hold, without fetching it'. It does not name the alternative tool explicitly, but the scope boundary is clear enough to tell the two apart.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'without fetching it' functions as a selection condition: use this when you already possess the text, otherwise the fetching variant applies. It gives clear context but never explicitly names the sibling or states exclusions, so it falls short of a 5.

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