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

The description adds valuable context beyond annotations: results are similarity-ranked candidates rather than verified answers, embeddings require AI processing, and the tool requires a Word Orb key and consumes quota. It does not contradict any annotation, though it could further clarify potential nondeterminism or latency 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?

Three sentences, each earning its place: purpose, result nature, and operational requirements. Information is front-loaded and no filler is present.

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 search tool with a well-documented schema, the description covers purpose, result caveats, and quota/auth requirements. The absence of an output schema and any description of return format is a minor gap, but the essential decision-making information is present.

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 description coverage is 100%, so the schema already documents both 'query' and 'limit' adequately. The description restates the core idea of a natural-language concept query but adds no parameter-specific detail 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?

States a specific verb ('Search') and resource ('available English word embeddings') and clarifies it searches by concept description, not by exact word. The phrase 'Returns similarity-ranked candidates, not verified answers' distinguishes it from lookup/search tools among 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?

The description implies this is for concept-based semantic search rather than exact word lookup, but it does not explicitly name sibling alternatives like search_words or lookup_word or state when to prefer them. The intended use case is reasonably clear from 'description of a concept,' but exclusion guidance is missing.

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