AI Crawler Access Check

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds substantial behavioral context: it resolves 'every known AI/LLM crawler,' distinguishes citation vs. training bots, reports advisory vs. enforceable blocks, discloses vendor quirks (e.g., Applebot following Googlebot rules), and notes provenance of crawler tokens. This far exceeds the safety profile already conveyed by annotations.

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 long but front-loads the core purpose in the first sentence, then organizes coverage, differentiators, parameters, returns, and example in a logical, scannable structure. Every sentence adds information, though the bulleted 'Three things' and extensive crawler list make it somewhat verbose; still, it earns its length through specificity.

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 tool's complexity, the description is exceptionally complete. It explains nuanced behavior, provides a structured return object, lists all four parameters with defaults, includes a usage example, and even anticipates edge cases like undocumented vendors. The presence of an output schema (the 'Returns' block) further covers return semantics. No notable gap remains for an agent to understand what the tool does and when to invoke it.

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?

With 100% schema description coverage, parameters are fully documented in the schema. The description's 'Args' section largely duplicates the schema (site, path, include_deprecated, response_format) and adds only minimal color (e.g., 'retired tokens' for include_deprecated). No extra meaning, constraints, or usage nuances are contributed beyond the schema, so 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?

The description opens with a specific, active statement: 'Resolve every known AI/LLM crawler against a site's robots.txt and report which may fetch a given path.' This clearly identifies the tool's function and distinguishes it from generic robots.txt checkers, further reinforced by the detailed list of covered crawlers and unique analytical angles (training vs. citation, enforceability, vendor quirks).

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 provides a concrete usage example ('Can ChatGPT and Perplexity crawl example.com?') and implies value over naive robots.txt readers with its 'Three things this gets right' section. However, it does not explicitly name sibling alternatives or state when NOT to use this tool, so it stops short of full guidance.

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