Fetch AI-parsed page data

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

With no annotations, the description carries the burden and does well: it discloses the ScrapeUnblocker routing layer, the output form, and a non-obvious billing rule for empty results. It does not address authentication, rate limits, or latency, so it falls short of full disclosure.

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?

Three sentences, zero padding, and the core differentiator (structured JSON vs raw HTML) is front-loaded before the usage guidance and the fallback instruction.

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?

With no output schema, the description usefully characterizes the return value (structured JSON with product/article fields) and the empty-result case. It is nearly complete, though it leaves the exact response envelope unspecified.

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 url, rules_hint and proxy_country are already documented in the schema. The description hints at the parsing intent behind rules_hint but adds no syntax or format detail beyond what the schema provides, so baseline 3 applies.

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?

States a specific verb+resource (fetch a web page) and the distinctive output (AI-parsed structured JSON instead of raw HTML), with concrete examples of the content types returned. It explicitly distinguishes itself from the sibling fetch_html.

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?

Names the ideal scenarios (product, listing, article pages) and names the alternative tool (fetch_html) with the condition that selects it. It also covers the failure case - no structured data present - and notes that call is unbilled, which removes ambiguity about retry behavior.

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