ask

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

No annotations are supplied, so the description carries the full disclosure burden. It does add real behavioral context — the model is server-configured rather than selectable, and a /* URL suffix expands to the whole site — but it omits live-fetch vs cache behavior, cost/latency, URL count limits, and failure handling for unfetchable pages.

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 dense sentences with no filler, front-loading the core action before the URL and search modifiers. Slight cost in clarity: the /* rule is introduced with 'An address' without naming the urls parameter, which forces a small inference.

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 annotations and no output schema, the description should say more about what comes back — particularly that a supplied schema shapes the response, or how free-form answers are returned. It covers inputs and one scoping rule well but leaves output behavior and operational limits undefined for a 4-parameter AI-backed tool.

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 coverage is 0%, so the description must compensate, and it substantially does: prompt maps to 'answer a question', schema to 'or fill a JSON schema', urls to 'from pages' with the /* wildcard rule, and web_search to 'adds the top search results'. All four parameters get some semantic grounding, though the expected shape of the schema object and any constraints on urls (count, format) remain unstated.

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-and-resource pair: answer a question (or fill a JSON schema) from pages using the configured AI model. An agent can tell it is an LLM-over-provided-URLs operation rather than a raw fetch. It does not name or differentiate itself from siblings like extract, search, deep_research, or agent, which overlap heavily with this capability.

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?

Usage is only implied: the mention that web_search adds top search results hints at when to enable it, and 'from pages' implies the user supplies URLs. There is no explicit when-to-use, when-not-to-use, or pointer to an alternative sibling such as extract for structured extraction or deep_research for multi-step investigation.

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