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

Beyond readOnlyHint and openWorldHint, the description discloses what is actually inside the payload (both llms.txt and llms-full.txt bodies) and adds a security-critical instruction that the bodies are third-party text to be treated as quoted data, not instructions. That prompt-injection warning is behaviorally important and appears nowhere in the structured fields.

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, front-loaded with the action and payload, then the safety caveat. Every clause earns its place with no redundancy.

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?

An output schema exists, so return-value documentation is not required. The description nonetheless names the key fields returned and closes the one behavioral gap that matters for an open-world read (untrusted embedded text), leaving nothing an agent needs to call it correctly.

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 single 'domain' param is already documented with an example ('shopify.com'). The description's 'by domain' only reinforces the schema, adding no new syntax or format detail, so the baseline 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 ('Return') and resource ('one site's AI-readiness record by domain'), and enumerates the payload ('stored llms.txt and llms-full.txt bodies'). This clearly distinguishes it from the sibling datasets_llms_search (search) and datasets_llms_facets (facets), which are the only other llms tools.

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 'by domain' implies a direct single-record lookup once the caller already has a domain, which is usable context. However, it never states when to prefer this over datasets_llms_search nor how a domain would be obtained, so usage is only implied.

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