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

Beyond the annotations, the description discloses extraction sources, obfuscation handling, Cloudflare-protected pages, JSON-LD parsing, share-link ignoring, same-domain tagging, crawl depth, SSRF-safe fetching, legal considerations, pricing, and an alternate HTTPS endpoint. This is unusually rich behavioral context for an agent.

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 front-loaded with the core purpose and every detail is relevant, but the first sentence is a long, dense list with parentheticals. It is efficient for a complex tool yet could be easier to scan.

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?

For a complex extraction tool with no output schema, the description covers sources, crawl behavior, safety, legal use, and pricing. It still does not describe the return-value shape or result structure, which would be helpful since no output schema exists.

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%, so the schema already documents url, crawl, and max_pages. The description adds context about following up to 3 same-site pages by default, but does not provide parameter-level meaning beyond what the schema already conveys.

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 states a specific verb and resource: extract public business contact details from a website. It enumerates the exact artifact types (emails, phones, social profiles, JSON-LD org data, contact-form URLs), which clearly distinguishes it from generic siblings like web_to_text or url_links_extract.

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 usage context is clear: extract public business contacts, respect GDPR/CAN-SPAM, and optionally crawl same-site contact/impressum/about pages. It does not explicitly name alternatives such as pii_detect for personal data or url_links_extract for generic link collection, so it stops short of full when-to-use/when-not-to-use routing.

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