Venice Embeddings

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

With no annotations provided, the description carries the behavioral burden. It discloses auth options ('Supports x402 wallet auth (no Venice account needed) and API key'), which is valuable operational context. It also establishes the OpenAI-compatible interface. It doesn't mention rate limits or output format, but for an embeddings tool the key behavioral aspects (auth, batch preference) are covered.

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: the core purpose, operator preferences for model selection, and auth requirements. No fluff or redundancy. Extremely efficient.

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 with 3 params, 67% schema coverage, and no output schema, the description reasonably covers the essentials: purpose, default model, batch preference, and auth. It could mention the return format (embedding vectors) but since no output schema exists, some return-type guidance would strengthen this. Still, it's reasonably complete for a straightforward embeddings API wrapper.

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 coverage is 67% (2 of 3 params get descriptions: input and model are described, encoding_format has an enum listing 'float'/'base64' but no prose). The description mentions text input and the OpenAI-compatible nature, but adds minimal detail beyond schema. The default model mention adds some value for the model param, but encoding_format semantics are only implied by the enum.

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 'Compute embeddings for text input (OpenAI-compatible)' using a specific verb+resource pairing. It distinguishes itself from sibling tools like venice_chat and venice_responses by specifying the embedding domain. However, it doesn't explicitly differentiate from potential embedding-related siblings, though none appear in the list given.

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 useful selection guidance: 'Operator preferences — default: text-embedding-3-small; also try: gemini-embedding-001' and notes 'Operator batch preferred; OpenAI-compatible.' This gives the agent context on which model to choose and batching preference. It doesn't explicitly state when not to use the tool or name alternatives, but the operator-preference guidance is substantive.

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