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

Annotations already declare a safe read, and the description still adds real behavioral context: syllable counts are rule-based so grades are estimates, and roughly 70 jargon/formal terms are built in. These caveats materially change how an agent should interpret results.

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 dense paragraph, front-loaded with the action and the required parameter, then the output contents. No filler sentences; even the trailing English-only caveat earns its place.

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 the description fully compensates by listing every returned metric plus a caveat about estimate accuracy. An agent can invoke this confidently without further documentation.

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 goes further by explaining that jargon is added to an existing built-in list of about 70 and that targetGrade defaults to 8, which clarifies intent beyond the raw 'Extra jargon words to flag' schema text.

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 a specific verb and resource ('Check text for plain language') and then enumerates exactly what it evaluates — Flesch Reading Ease, Flesch-Kincaid grade, passive count, jargon, wordy phrases. That surfaces it as a readability/lint tool, distinguishable from siblings like review_microcopy or label_clarity_check.

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?

It gives the operating context an agent needs — 'English only', targetGrade defaults to 8, and jargon accepts your own words on top of the built-ins. It does not name alternatives or say when not to use it, so it falls short of a 5.

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