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

With no annotations, the description carries full disclosure responsibility. It goes well beyond basics by stating the 10-credit cost, explaining that the model is not called on low-content pages, describing the low_content flag as a signal, and explicitly warning against fabricated answers. This is strong behavioral transparency.

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, then adds a use-case sentence, a critical low-content behavior note, a returned-fields list, and clearly labeled args. Every section earns its place; it is only as long as needed given the important behavioral caveats.

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?

This is complete for a two-parameter tool with no annotations and no output schema. It explains when to use it, what it returns, what to gate on, cost implications, and provides argument examples. There is no missing information an agent would need to call it correctly.

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 0%, so the description must compensate. It documents both parameters: url, and prompt with concrete examples of extraction tasks. The prompt semantics are enriched with examples, and the URL is at least identified as the target of extraction, which is sufficient for two simple string parameters.

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 ('Fetch a webpage and extract specific information using AI') and explicitly frames the tool as returning structured data rather than raw content, which distinguishes it from the sibling fetch tool. It is immediately clear what this tool does and what it is for.

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?

It explicitly says to use this when you need structured data (e.g. pricing, specs, contact info) 'rather than the raw content', which gives clear usage context relative to fetch. It doesn't mention research/search as alternatives, but the structured-vs-raw contrast provides enough guidance for an agent to select it correctly.

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