dictionary_related

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

With no annotations, the description bears full burden. It details the core algorithm (cosine similarity on 300d embeddings) and fallback behavior, adding value. However, it doesn't mention side effects, prerequisites beyond installation, or performance implications.

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 with no wasted words. Front-loaded with the most important information (method and fallback). Highly efficient.

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?

No output schema exists, yet the description does not describe the return format or structure. It only says 'semantically related words.' The fallback behavior is mentioned, but output details are missing, leaving the agent underinformed.

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 coverage is 0%; no parameter descriptions exist. The description mentions word, language, and limit implicitly but does not explain their semantics or constraints. It adds minimal value beyond the schema.

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 gets semantically related words using Numberbatch embedding cosine, a specific method. It distinguishes from sibling tools by mentioning denser embeddings and fallback to ConceptNet RelatedTo.

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 provides context about the method (dense embeddings vs ConceptNet) but does not explicitly state when to use this tool over siblings like dictionary_semantic_neighbors or dictionary_all_relations. No usage exclusions or alternatives are stated.

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