zen_readability

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

With no annotations, the description provides transparent algorithmic details (text density, paragraph count, inverse link density, stripping nav/aside) and fallback logic. However, it omits error cases or whether the operation is read-only.

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 concise: a clear one-line purpose followed by a brief explanation of the heuristic. Every sentence adds value, no redundancy.

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?

While the algorithm is explained, the description omits the output format (e.g., JSON, plain text) and does not clarify the tab_id parameter usage. Given no output schema, these gaps reduce completeness for an extraction 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?

The description does not mention the single parameter tab_id (integer or null, default null). Schema coverage is 0%, and the description adds no meaning beyond the schema, which is insufficient for a parameter that controls which tab to act on.

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 the tool extracts the main article (title, byline, clean text). It uses a specific verb ('extract') and resource ('main article'), distinguishing it from siblings like zen_page_text or zen_markdown.

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 article extraction via heuristic (text density, paragraph count, inverse link density) and fallback behavior, but lacks explicit guidance on when to use vs alternatives or exclusions.

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