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

Annotations already cover read-only and open-world behavior, so the description focuses on adding value: it fetches a live site and returns a 0-100 score plus the failed checks. That gives the agent output shape and result semantics, which is meaningful since no output schema exists. It omits failure modes such as unreachable URLs or timeouts.

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 tight sentences, front-loading the operation and then the checks and return value. Nothing is redundant with the title and no filler is present.

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 read-only audit with no output schema, the description covers what is checked and what comes back (score plus failed checks). It stops short of describing error behavior or the score's scale semantics beyond the 0-100 range.

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 the single url parameter already documents format and an example. The description adds nothing about the URL beyond what the schema provides, so the 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?

States a specific verb (check) and resource (AI crawler readiness of a website), and enumerates the concrete checks it performs (bot access for named crawlers, llms.txt, schema markup, server-rendered content). An agent can distinguish this from validate_llms_txt (a single sub-check) and check_ai_citations without opening any schema.

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 context of use is clear: a site-level readiness audit for AI crawlers. It does not explicitly name the siblings or say when to prefer validate_llms_txt over this broader check, so routing is implied rather than stated.

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