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

No annotations are provided, so the description carries the full burden. It discloses what the tool computes (effectively the output surface), which establishes that this is a non-mutating analysis call, but it says nothing about input constraints such as maximum text length, language applicability of the readability formulas, or handling of HTML/markdown.

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 sentences, front-loaded with the metric list and closing with the use case. The metric enumeration is long but each item is a genuine capability an agent may need to match against a request; minor overlap with the output schema is the only waste.

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?

An output schema exists, so return values need not be spelled out (the description does so anyway). For a single-parameter, pure-computation tool this is close to complete; only input limits and language assumptions are missing.

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?

Single required 'text' parameter with 0% schema description coverage. The description implies the input is the text being analyzed but adds no detail on format, size limits, or language expectations beyond the parameter name itself.

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 the resource ('Statistics for a text') and then enumerates the exact metrics produced: words, sentences, characters, paragraphs, reading/speaking time, Flesch, Flesch-Kincaid, Gunning fog, complex-word share, lexical diversity, top keywords. No sibling tool (define_word, word_finder, detect_language, hash_text, etc.) overlaps, so an agent can route to it unambiguously.

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?

'Use to check copy, essays or SEO content' gives clear usage context that maps to a concrete scenario. It doesn't state exclusions or alternatives, but no sibling covers text analytics, so there is little to disambiguate against.

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