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

With no annotations, the description carries the behavioral burden. It discloses public-page scope, boilerplate stripping, and text/Markdown output, but does not cover error behavior, rate limits, dynamic pages, paywalls, or authentication boundaries.

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?

A single front-loaded sentence states the fetch, the return value, and the stripping behavior. Every phrase is relevant and nothing is padded.

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 and no annotations, so the description must explain return behavior and constraints. It does so at a high level for text/Markdown and boilerplate stripping, but omits the json format and failure/edge cases, leaving gaps for an agent to infer.

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 50%, so the description should compensate for the undocumented format parameter. It mentions text or Markdown but omits the schema's json option and does not clarify what json returns, leaving the parameter only partially explained.

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 states a specific action and resource: fetch a public web page and return its main article text or Markdown. It also clarifies the transformation via boilerplate stripping, so an agent can tell exactly what the tool does.

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 'public web page' and 'main article text' wording implies use for public article extraction, but there is no explicit when-not guidance or alternative tool to compare against. The public-only constraint is a useful prerequisite but falls short of full usage routing.

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