dictionary_semantic_neighbors

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

Discloses key behavior: embedding-based, multilingual (78 languages), sorted by cosine similarity. With no annotations, this is good but could mention constraints like computational cost or that it returns only words (no scores).

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 concise sentences with key information front-loaded. No unnecessary 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?

Adequate for a search tool but missing output format details (e.g., returns list of words and/or similarity scores). Given no output schema, description should elaborate on what the return looks like.

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 covers language and targetLanguage with descriptions. Description adds context (Numberbatch, cosine similarity) but does not explain word format or limit behavior. At 50% schema coverage, description partially compensates but not fully.

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?

Description clearly states it returns embedding-based nearest neighbors across same or different languages, sorted by cosine similarity. This distinguishes it from siblings like dictionary_synonyms or dictionary_related, which are not explicitly embedding-based or cross-lingual.

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?

Description implies it is for semantic similarity and cross-lingual search, but does not explicitly state when to use it versus alternatives like dictionary_synonyms. No guidance on when not to use.

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