Content Quality Analysis

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

Annotations already mark the tool as read-only, open-world, and idempotent. The description adds meaningful behavioral context by stating that content is read from <main>/<article> landmarks to avoid inflated counts, and that both English and Spanish stopwords are filtered. It does not address edge cases like missing landmarks, but goes beyond the annotation baseline.

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 efficiently organized: a definitional first paragraph, a behavioral note, then clean 'Args' and 'Returns' sections, and a concrete example. Every sentence contributes, with no fluff or repetition.

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 2-parameter tool with an output schema, the description fully covers purpose, key behavior, return structure, and a usage example. The output schema and annotations cover safety and return types, while the description fills in what is not explicitly structured, making the tool self-contained.

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%, and the description's parameter section mostly mirrors the schema ('response_format' defaults and enums). The natural-language example adds illustrative value but does not clarify any new parameter semantics beyond what the schema already provides.

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 opens with 'Measure the page's main content' and itemizes precise metrics (word count, Flesch reading ease, text-to-HTML ratio, thin-content detection). This specific verb+resource clearly distinguishes it from sibling tools like seo_audit or meta_tags_check, which focus on different aspects.

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 example 'Is the content on https://example.com/post too thin?' provides a concrete use case, and the landmark-reading note explains a relevant context (ignoring chrome). However, it does not explicitly contrast with alternatives such as heading_structure or link_audit, so when-not-to-use guidance is missing.

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