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

With no annotations, the description carries the full burden and does so: it discloses determinism twice, states no LLM is involved, and proactively flags the syllable-counting approximation ('standard vowel-group heuristic... not a dictionary lookup'). That caveat is exactly the kind of behavioral trait an agent can't infer from structured fields.

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?

Front-loaded with purpose and output set, then caveats and the sibling distinction in descending priority. Slightly dense and the determinism claim is restated at the end, but no sentence is wasted.

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?

No output schema exists, so the description must enumerate return values — it does (counts, reading-ease band with its range, optional on_target flag). Combined with the heuristic caveat and sibling disambiguation, an agent has everything needed to call and interpret it.

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 baseline is 3; the description earns more by explaining the effect of target_grade — it produces an on_target flag and the tolerance is +/-2.0 grade levels, which the schema does not state. The 100,000-character limit is covered by the schema rather than repeated.

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?

Names the exact metrics (Flesch Reading Ease, Flesch-Kincaid Grade Level) and the exact input (a block of text), plus the auxiliary outputs it returns. It also explicitly separates itself from the sibling humanizer_score, so an agent can choose between them without opening a schema.

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?

Explicitly routes the agent away from the adjacent metric family ('Different metric family from humanizer_score - this is pure readability statistics, not AI-writing-tell detection') and explains the optional target_grade path. It stops short of stating when-not-to-use (e.g. non-prose input) but the context is clear.

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