nv_embed

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

The description discloses a potential side effect (writing vectors to disk via save_to) and notes the default model behavior. However, with no annotations, it does not address permissions, reversibility, or error behavior, leaving gaps in the operational picture.

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 succinct: two introductory sentences plus a compact Args block. It is well-structured, front-loaded with the main action, and every sentence adds useful information without redundancy.

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 tool of this simplicity, the description covers the primary function, optional side effect, and parameter meanings. The existence of an output schema reduces the need to detail return values, though some edge-case behavior like input size limits or error handling is not mentioned, keeping it from a 5.

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?

All three parameters (texts, model, save_to) are explained in the Args section, providing meaning beyond the bare schema. This is especially valuable given the schema has no descriptions, and the explanations are clear and sufficient.

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 embeds texts and mentions that it returns shape and stats, which conveys the core function. However, it does not explicitly distinguish itself from sibling tools like nv_similarity or nv_call, so it lacks clear differentiation.

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?

There is no guidance on when to use this tool over alternatives. It only mentions that the model parameter defaults to the best available one, but does not explain ideal use cases, prerequisites, or exclusions.

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