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

Annotations already declare the operation is read-only, non-destructive, and open-world. Beyond that, the description adds useful behavioral detail: it distinguishes answer-engine versus training crawler access in robots.txt, treats llms.txt and ai.txt as informational signals, and states that the output includes an A-F grade, per-crawler access, and findings. It does not mention rate limits or caching, but its behavioral disclosure is substantially richer than the annotations alone.

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 compact and front-loaded: the purpose and resource come first, followed by the specific files checked and the output shape. Every clause adds useful information, and there is no filler or repetition.

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 grading tool with no output schema, the description supplies enough context to call it correctly and to understand what will be returned. It identifies the public files analyzed, the grading approach, and the output components (grade, per-crawler access, findings), leaving no material gaps for an agent.

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?

There is only one parameter, 'domain,' and the input schema already documents it with an example. The description adds the context that the domain determines which public files are fetched and graded, but it does not add format, normalization, or validation semantics beyond the schema. With 100% schema coverage, a 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 uses a specific verb, 'Grade,' with a clearly defined resource: how discoverable a website is to AI answer engines. It enumerates the exact public files examined (robots.txt, sitemap.xml, llms.txt, ai.txt) and names the answer engines considered, making the tool's scope distinguishable from email and domain siblings.

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 gives a clear context for use: assessing AI answer-engine discoverability from public files. It does not explicitly state when not to use the tool or name an alternative, but the sibling tools focus on email authentication, blacklists, domain age, redirects, and WHOIS, so the use case is unambiguous by contrast.

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