create_embeddings

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It only restates the conversion action and lists use cases; it does not mention default model behavior, truncation handling, output format specifics, rate limits, or other operational traits. This is a limited disclosure beyond the name.

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 a single, well-structured sentence that front-loads the core action and resource before adding valuable use-case context. Every word earns its place; there is no redundancy or filler.

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?

With no output schema and no annotations, the description does not explain return value shape, default model/truncation settings, or how the optional parameters affect behavior. It is adequate for a high-level understanding but insufficient for reliable invocation in complex scenarios.

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?

The schema describes 'input' and 'model', but 'truncate' and 'encoding_format' are only enums without detailed descriptions. The tool description adds no parameter-level meaning and does not compensate for the 50% schema coverage gap. It merely restates that text is converted, which is already implied by the 'input' parameter.

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 uses a specific verb ('Convert') and identifies the exact resource ('text(s) into vector embeddings using NVIDIA NIM embedding models'). It clearly distinguishes from sibling tools like chat_completion and rerank_passages by stating the core function.

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 concrete use cases ('semantic search, RAG, clustering, and similarity comparisons') that signal when to use this tool. It lacks explicit exclusionary guidance or comparison to alternatives, but the context is clear enough for the agent to select it appropriately.

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