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

Annotations already cover readOnlyHint, openWorldHint, and idempotentHint. The description adds that it uses AI extraction and returns JSON with camelCase keys, which is useful. However, it does not disclose potential latency, error behavior, or rate limits, which would be valuable given the external service dependency.

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 two sentences plus an inline example, front-loaded with the core purpose. Every sentence adds value, and the example is concise without fluff.

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 5-parameter tool with all simple strings and no output schema, the description covers the essential usage: how to specify fields and what to expect in return. It lacks details on error handling or constraints but is adequate for the tool's simplicity.

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 parameters are already documented. The description adds a concrete example and clarifies how extract_properties maps to camelCase output keys, going beyond the schema's generic description and reinforcing correct usage.

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 clearly states the verb 'extract', the resource 'structured data from a web page', and the mechanism via extract_properties. It implicitly distinguishes from siblings like scrapingant_markdown and scrapingant_scrape by focusing on structured AI extraction, making the purpose unambiguous.

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 provides a usage example but does not explicitly state when to choose this tool over alternatives such as scrapingant_scrape or scrapingant_markdown. There is no mention of conditions, exclusions, or comparative guidance, leaving selection to inference.

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