nv_similarity

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

With no annotations, the description carries the full burden. It discloses a useful behavioral detail: the model parameter 'Defaults to the best available one', indicating automatic model selection. However, it does not address side effects, permissions, rate limits, or error conditions, leaving some gaps in transparency.

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 extremely concise and front-loaded with the main purpose in the first sentence. The Args section is structured and each line serves a clear function, with 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 read-only ranking tool, the description covers the essential inputs and behavior. Since an output schema exists, the lack of return-value explanation is acceptable. Minor gaps like empty-candidate handling or model availability are not addressed, but the overall completeness is strong given the tool's simplicity.

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 0%, so the description must compensate. It provides concise definitions for all three parameters: query as 'the search string', candidates as 'texts to rank', and model as 'embedding model id' with a default behavior. This adds meaningful context beyond the bare schema titles, though it lacks detailed constraints or examples.

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 opens with 'Rank candidate texts by semantic similarity to a query', which is a specific verb+resource action. It clearly distinguishes this from sibling tools like nv_embed (embedding generation) or nv_call (direct invocation), making the purpose unambiguous.

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 usage context through its phrasing ('Rank candidate texts...'), but it does not explicitly state when to use this tool versus alternatives. No exclusions or alternative tool mentions are provided, so the guidance remains only implicit.

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