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

The description is highly effective at detailing the underlying methodology, such as checking for content that reads 'without JavaScript,' which is a critical behavioral trait beyond simple annotation. It provides a comprehensive view of the technical depth of the audit process.

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 verbose. It reads like a comprehensive feature checklist rather than a summary, listing every single element (headings, canonical, JSON-LD, etc.). While comprehensive, the excessive length sacrifices conciseness.

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 complexity of modern search and AI indexing, this description is maximally complete. It covers technical structure, content requirements, and indexing signals, providing a complete picture without relying on any external documentation.

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?

Although the schema provides 100% coverage, the description adds semantic context by detailing how those parameters are processed; for instance, it specifies that the tool analyzes `robots.txt` and `sitemap`—processes that occur when the `url` parameter is input.

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 is extremely specific, defining its purpose across three distinct technical categories (SEO, AEO, and GEO). It goes far beyond a simple summary by listing precise elements like 'robots.txt for search and AI crawlers' and 'JSON-LD,' making it immediately clear what the tool accomplishes.

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?

While it does not explicitly name when *not* to use the tool compared to its siblings, the extensive detail about the specific types of audits (e.g., checking alt tags, share cards, and server-rendered text) clearly segments its usage for content/structure auditing, implicitly narrowing its scope against other tools.

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