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

Annotations are minimal (only a title), so the description carries the full burden of behavioral disclosure. It mentions AI-powered and JSON return, but does not disclose potential non-determinism, error handling, rate limits, or side effects. This is insufficient for a tool with no other behavioral hints.

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 with no unnecessary words. It front-loads the core action and return type, and every word earns its place. This is an appropriately sized description.

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 output schema, the description should explain return values thoroughly, but it only says 'returns JSON matching your prompt or schema.' It omits critical details about wait_ms, wait_for, error scenarios, and how to construct the schema, making it incomplete for a 5-parameter 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 description coverage is 60% (url, prompt, schema), but wait_ms and wait_for are undocumented. The description does not compensate for these gaps; it only references 'prompt or schema' and adds no meaning beyond the schema. Since coverage is not high, the description should add value, but it doesn't.

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 a specific action—AI-powered extraction of structured data from any webpage using natural language—and distinguishes it from siblings like scrape_webpage and extract_metadata. The verb 'extract' plus the resource 'structured data' makes 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 implies usage for structured data extraction but does not explicitly state when to use this tool over alternatives such as scrape_webpage or extract_metadata. No exclusions or alternative recommendations are provided, so it only reaches the 'implied usage' level.

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