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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that the tool returns structured JSON and shows an example call, which is helpful. It does not disclose failure modes, rate limits, or what happens when extraction fails, but given the annotation coverage, this is an acceptable level.

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 a compact example. It front-loads the core action, states the key mechanism (ai_query), and includes a realistic invocation with required parameters. No wasted words or redundant schema repetition.

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?

There is no output schema, but the description explicitly says the tool returns structured JSON, which covers the main return value need. The example also demonstrates required parameters. It lacks guidance on error cases, rate limits, or how the output structure relates to ai_query, but for a straightforward extraction tool this 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 description coverage is 100%, so the schema already documents every parameter. The description adds a concrete usage example and re-emphasizes that ai_query is a natural-language description, but it does not meaningfully supplement the schema's parameter explanations. This matches the baseline for full schema coverage.

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 uses a specific verb+resource: 'Extract structured data from a web page using ScrapingBee's AI.' It clearly distinguishes this from sibling tools like scrapingbee_scrape or scrapingbee_google by emphasizing natural-language extraction via ai_query. The included example reinforces the intended use case.

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 implies when to use the tool: when you want structured data from a page via a natural-language query. However, it does not explicitly state when not to use it or mention alternatives like scrapingbee_scrape, scrapingbee_google, or other research tools. The example shows how to call it, but usage boundaries are left to inference.

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