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

Annotations only declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false; the description adds genuinely new behavior: the call spends account balance billed per input token, usage.cost_rub reports the cost, and vectors are large (~1536 numbers, ~30 KB of JSON) landing in the agent's context. That cost and context-bloat warning is exactly the kind of disclosure annotations cannot carry.

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 tight paragraphs, front-loaded with what the tool does, then usage guidance, then cost/behavior. Every sentence carries distinct information — use cases, batching advice, size warning, billing — with no restatement of the name or schema.

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?

No output schema exists, and the description compensates by describing the return shape (OpenAI-style, data[i].embedding is the vector for input[i], usage.cost_rub for cost). For a three-parameter tool with full schema coverage, nothing an agent needs to call it correctly is missing.

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 100%, so the baseline is 3, but the description adds value beyond the schema: it explains why to pass a list (faster and cheaper than one call per string) and when to use `dimensions` (only where the model allows it). It does not add new syntax or formatting beyond what the schema states, so it stays just above baseline.

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?

States a concrete verb+resource (turn text into embedding vectors) and enumerates the workloads it serves — semantic search, RAG, deduplication, clustering — which cleanly separates it from siblings like chat_complete, rerank, and image_generate. It also pins the resource to an OpenAI-compatible shape, so an agent knows exactly what category of operation this is.

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?

Gives clear when-to-use context (embed what you will actually store or compare) and routes the agent away from this tool for large batches: 'send large batches through the HTTP API (POST /v1/embeddings)'. It also advises batch-in-one-call over per-string calls. There is no explicit when-not beyond the large-batch case, but the routing guidance is concrete.

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