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

No annotations are provided, so the description carries the full burden. It discloses the tool is deterministic, uses no LLM, provides signed output, lists data sources, and states cost. It does not cover failure modes or caching, but for a simple audit tool, the key behaviors are transparent.

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 extremely concise—two sentences plus a cost note. It front-loads the core purpose and provides essential context without 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?

Given the simple nature of the tool and high schema coverage, the description adequately covers what the tool does and its key characteristics. However, it lacks details about the return format (e.g., structure of the signed verdict), which would be helpful since there is no output schema.

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% with both parameters (path and domain) described. The description does not add significant semantic value beyond the schema; it repeats the domain concept but no new details like default behavior for path.

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 clearly specifies the tool audits robots.txt, llms.txt, and ai.txt for per-AI-crawler allow/block policies, producing a signed, timestamped verdict. It distinguishes itself from sibling tools like fetch or headers by focusing specifically on crawler policy evaluation.

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 implies usage for verifying crawler permissions and mentions EU-AI-Act evidence, but it lacks explicit guidance on when not to use or alternatives. Given sibling tools, the context is manageable, but clearer when-not statements would improve.

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