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

With no annotations provided, the description carries full disclosure burden and does so thoroughly: it states the exact URL fetched, the 10s timeout, validation rules (non-HTML content type and leading H1), the rejection of SPA HTML pages, and the not_applicable outcome.

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?

Every sentence carries distinct information: purpose, use case, fetch/validation behavior, and return shape. The description is detailed yet tightly written with no 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 one-parameter network probe with no annotations, the description covers input, network behavior, validity criteria, success return shape, and the not_applicable case. Nothing essential is missing for an agent to select and invoke 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 100%, and the domain parameter is fully documented in the schema with format and rejection rules. The description adds context by showing how the domain is embedded in the URL, but does not need to duplicate schema details.

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 opens with a specific verb and resource: 'Detect whether a domain publishes an llms.txt at its root.' It also defines what counts by requiring a non-HTML content type and a leading markdown H1, which distinguishes it from broad web-fetch checks like dossier_web_surface.

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 its context: 'Use in a content-posture audit to confirm a site guides (rather than ignores) AI agents.' It does not name exclusions or a sibling alternative, but the stated use case is clear enough for routing.

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