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

No annotations are provided, so the description carries the full burden. It discloses the multi-language behavior (one spelling returns entries in many languages), the content fields (senses, part of speech, etymology, pronunciation), and the coverage scope. It does not mention potential response size or errors, but this is a read-only lookup with no destructive effects, and the description gives a strong sense of what to expect.

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 longer than strictly necessary but every sentence earns its place by covering scope, multi-language behavior, filtering, and contrast with the sibling. It is front-loaded with the core purpose and packs detail efficiently without being rambling.

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?

Given there is no output schema, the description sufficiently explains what kind of information is returned (senses, POS, etymology, pronunciation). It also covers the main edge case of multiple language entries and how to handle it. It doesn't describe the exact return structure, but for a dictionary lookup this is adequate context for an agent to select and invoke the tool.

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 input schema already covers all three parameters (word, language, pos) with descriptions, so the base is 3. The description adds value by explaining that language is used to narrow the multi-language results (with example 'hund'), and that pos filters to one part of speech, reinforcing the schema semantics with concrete usage context.

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 clearly states the tool defines what a word means, with specific details on coverage (8.15M entries, historical and indigenous languages). It explicitly distinguishes itself from sibling lookup() by contrasting define('java') vs lookup('Java'), making the purpose unmistakable.

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 explicit guidance on when to use this tool ('Use this for words') versus lookup() ('and lookup() for things'), and explains how to narrow results using the language and pos parameters. This directly addresses the sibling alternative, which is exactly what usage guidelines should do.

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