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

With no annotations, the description carries the full burden. It states the core behavior (finding various relation types) but does not disclose potential edge cases like handling of unknown words, multi-word phrases, or the order/format of results. For a simple read operation, this is acceptable but not richly transparent.

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?

The description is a single, well-structured sentence that front-loads the action and lists the result types. No wasted words.

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 a simple tool with one parameter and no output schema, the description adequately conveys the tool's purpose and result types. It could mention that results are returned as a list or grouped, but the omission is not critical.

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?

The schema has 100% coverage for the single parameter 'word', but the description enriches it by specifying the kinds of relations returned (semantic, rhyme, root, knowledge graph). This adds meaningful context beyond the schema's simple 'find relations for'.

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 uses a specific verb 'Find' and names the resource: semantic neighbors, rhymes, same-root words, and knowledge graph connections. This clearly distinguishes it from sibling tools like lookup_word or define_term, which focus on definitions or translations.

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?

The description implies when to use this tool (when you need related words rather than definitions or translations). It doesn't explicitly mention alternatives, but the context is clear enough given the sibling list and the tool's specific focus on word relations.

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