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

Goes well beyond the readOnly/openWorld annotations: discloses the vowel-group syllable heuristic as an accuracy caveat, a 100 KB input ceiling, per-call price ($0.002 USDC via x402 on Base/Solana/Arc), and that failed calls are not charged. This is exactly the operational context an agent needs before spending money on a call.

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?

Effectively three dense sentences, front-loaded with the deliverable, then caveats, then cost. Slightly packed (formula list is long) but nearly every clause carries distinct information.

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?

With an output schema present the description needn't describe return shape; it covers accuracy limits, size limit, language behavior, and pricing. Nothing material for a correct, cost-aware invocation is 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?

Schema description coverage is 100%, so both `text` and `language` are already documented. The description restates the same language behavior (English formulas vs. counts-only for other codes) and the 100 KB limit, adding little beyond the schema — baseline 3.

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 (readability of a text) and enumerates the exact formulas returned (Flesch, FK grade, Gunning Fog, SMOG, Coleman-Liau, ARI), which distinguishes it cleanly from adjacent text_* siblings like text_grammar or text_keywords.

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?

Implied usage is clear from the metric list, and the 'English formulas; other languages get counts only' clause gives one conditional rule. However, it never names an alternative tool or states when to prefer a different text_* sibling, leaving the agent to infer routing.

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