GEO / AI Answer-Engine Readiness Audit

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description reveals key behavioral details: it weights specific criteria, it detects pages invisible to AI assistants due to JS hydration, and it distinguishes AI search crawlers from training crawlers in robots.txt. It also transparently explains why llms.txt is reported but not scored. This adds substantial context beyond the structured 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 well-structured and information-dense without being verbose. It leads with the core purpose, then presents the weighted criteria, unique capabilities, args, returns, and a clarifying note about llms.txt. Every sentence adds value, and the format is easy to scan. No unnecessary fluff or 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?

The description is comprehensive for a tool with this complexity. It explains the scoring dimensions, the return structure (geo, rendering, crawler_access, etc.), and the rationale behind not scoring llms.txt. The presence of an output schema is complemented by a clear summary of the returned fields. It also provides an example that ties the tool to a realistic query. All essential context is covered.

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?

The input schema provides 100% coverage of parameters, including descriptions for url and response_format (with enum values and default). The description repeats the parameter list but adds minimal new semantics beyond the schema. It shows an example call but does not clarify details like URL scheme defaulting, which the schema already covers. Since the schema carries the parameter documentation, a score 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 clearly states the tool's purpose: scoring how readily an AI answer engine can fetch, parse, and cite a page. It specifies the resource (a URL) and the action (scoring readiness), and it distinguishes itself from classic SEO tools by highlighting two unique capabilities. This makes it easy to understand what the tool does and how it differs from 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 provides a clear use case via an example ('Is https://example.com/guide ready to be cited by ChatGPT?') and explains what it catches that classic SEO tools do not. However, it does not explicitly name alternative sibling tools or state when NOT to use this tool in favor of a more specific one like ai_crawler_access. The guidance is implied rather than explicitly contrasted with alternatives.

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