Fetch AI-parsed page data

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

No annotations are provided, so the description carries the full burden. It mentions 'through ScrapeUnblocker' and 'AI-parsed structured JSON' but does not disclose potential rate limits, failure modes, or processing delays. While core behavior is clear, additional behavioral context is missing.

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?

Two concise sentences: first explains the core functionality, second provides usage guidance. No filler, front-loaded, and every word adds value.

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 no output schema, the description provides examples of returned data (product details, article content). It lacks details on error handling or exact JSON structure, but for a simple fetch-and-parse tool, it is reasonably complete.

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 descriptions cover 100% of parameters, so the description does not need to repeat them. The description adds overall context (e.g., extracting fields without HTML parsing) but does not add specific parameter-level meaning beyond what the schema provides.

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?

Description clearly states the tool fetches a web page through ScrapeUnblocker and returns AI-parsed structured JSON, with examples like product details or article content. This distinguishes it from siblings fetch_html (raw HTML) and google_search (search).

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 says 'Best for extracting fields from product, listing or article pages without writing your own HTML parsing', which clearly indicates when to use it and implies when not to (e.g., raw HTML needed). It does not explicitly contrast with google_search, but context derived from sibling tool names is present.

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