extract

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

With no annotations, the description carries the burden and does well: it discloses that sanitization runs by default stripping hidden elements, comments, and zero-width characters (prompt-injection vectors), and that main_only narrows to the main content container. It does not cover behavior on malformed HTML or that the operation is a side-effect-free pure transform.

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 the transformation stated first and defaults/options after; minimal waste. Slightly clause-heavy but every clause carries information.

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 one-required-parameter transform with no output schema and no annotations, it covers purpose, defaults, sanitization behavior, and output kinds. Missing only return-shape specifics and failure behavior, which are minor here.

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 100%, so all six parameters are already documented. The description mainly reinforces the sanitization/raw interaction and main_only intent that the schema already states, adding only marginal nuance beyond the structured fields.

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 concrete transformation: converting HTML source into clean Markdown/body text/tables, with the goal of token-efficient reading. The input being raw HTML rather than a URL implicitly distinguishes it from scrape/crawl siblings, though no sibling is named explicitly.

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?

Gives conditional guidance (main_only=true is better for article pages; raw only when truly needed) but never states when to pick this tool over scrape, crawl, or auto. Usage is implied by the input type rather than spelled out.

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