Venice Embeddings

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

No annotations are provided, so the description must disclose behavioral traits. It mentions authentication methods but fails to disclose critical behavior such as return format, rate limits, data handling, or cost implications. For an embeddings tool, the return vector format and batch limits are essential.

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 redundant words. Information is front-loaded: first sentence describes function, second sentence adds authentication context. Every phrase earns its place.

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?

Despite having no output schema, the description does not explain return values or usage context. For a machine learning embeddings tool, key details like output vector dimensions, batch size limits, and typical use cases are missing. The description is too minimal for practical use.

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?

With schema coverage at 67%, the description adds value by stating 'OpenAI-compatible', which implies the parameters follow OpenAI's convention (model ID, input text, encoding format). This helps an agent infer parameter semantics beyond the schema's minimal descriptions.

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?

Description clearly states 'Compute embeddings for text input' with explicit verb 'Compute' and resource 'embeddings'. It also notes OpenAI compatibility, which distinguishes it from other Venice tools. With 30+ sibling tools, this clarity is effective.

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?

Description mentions two authentication methods (x402 wallet auth and API key) but provides no guidance on when to use this tool versus alternatives like venice_chat or venice_responses. No explicit when/when-not conditions or use cases.

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