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

Annotations already cover the safety profile (readOnly, non-destructive, openWorld=false). The description adds real behavioral context beyond them: random selection (consistent with idempotentHint=false), automatic exclusion of used words, and default exclusion of words ending in ん. It does not describe the return format.

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, purpose front-loaded, no filler. The pool sentence is dense but informative and correctly ordered after the core behavior.

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 an 8-parameter tool with no output schema, the description covers purpose and the main behavioral rules but leaves loose_dakuten entirely unexplained and gives no sense of what the returned suggestions look like. Adequate but with clear gaps.

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?

At 63% schema coverage the description does useful work by explaining each pool value (everyday vs common vs all), which the schema only lists as bare enums. It also clarifies the ん-ending exclusion tied to allow_n, though loose_dakuten and count remain undocumented anywhere.

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 gives a specific verb and resource: 'Pick random words that can follow the previous word (or start with a given kana)'. It's clear this is a suggestion/generation tool rather than a validator, which implicitly separates it from check_move and check_word, but it never names or contrasts with those siblings.

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?

Usage is implied through the 'after'/'start_char' alternatives and the exclusion of already-used words, but there is no explicit when-to-use, when-not-to-use, or reference to the sibling check tools. The agent must infer that this generates options while the siblings validate them.

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