check_site

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

With no annotations, the description carries full behavioral burden. It does disclose valuable behavior beyond the schema, namely that multiple resources are fetched per bot and that the result is a numeric score with remediation items, but it says nothing about latency, rate limits, or what happens when a site is unreachable or fully blocked.

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 filler, and the core purpose is front-loaded before the mechanism and return shape. 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?

There is no output schema, and the description correctly compensates by naming the return shape (score 0-100, list of fixes). For a two-parameter read-only diagnostic it is nearly sufficient; only failure-mode behavior is left unstated.

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%, so both url and live are already documented, and the description's 'as each AI bot' phrasing merely restates the live flag's meaning. Nothing in the description adds format, syntax, or validation detail 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?

States a specific verb ('Check') plus the exact resource and target platforms (ChatGPT search, Claude, Perplexity), then enumerates the artifacts inspected and the output form (0-100 score plus fixes). It is unmistakable against the sibling draft_llms_txt, which produces a file rather than diagnosing one.

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 diagnostic framing implies when to call it, but there is no explicit statement of context, prerequisites, or when to prefer draft_llms_txt instead. An agent must infer the use case, though the inference is an easy one.

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