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

Annotations already cover safety (destructiveHint=false, openWorldHint=true), so the bar is lower; the description adds the materially important fact that each successful call costs $0.002 in USDC on Base via x402. That billing/authorization detail is exactly the kind of context structured fields cannot convey, and it also explains why readOnlyHint is false for a pure computation tool. It does not discuss rate limits or failure/refund behavior, keeping it below 5.

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 compact sentences with no filler: capability first, then use cases, then cost. The cost constraint is placed last where it belongs and no sentence restates another.

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 single-parameter tool with no output schema, the description supplies the return shape (1024 dims), model provenance, language coverage, intended use cases, and payment terms. Nothing an agent needs to invoke 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?

With one parameter at 100% schema description coverage, the schema already documents the 'text' input and its 4,000-character limit, so the baseline is 3. The description adds only marginal meaning beyond it, mainly that 100+ languages are supported via BGE-M3, which slightly informs acceptable input but does not go beyond confirming the schema constraints.

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 names the exact output (a 1,024-dimension embedding vector), the underlying model (BAAI BGE-M3), and the input shape (up to 4,000 characters), which is a specific verb+resource statement. It is immediately distinguishable from siblings like ai_classify or ai_sentiment that also accept text but return labels rather than vectors.

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?

It enumerates intended applications – semantic search, RAG retrieval, clustering, deduplication – which gives the agent clear selection context. It stops short of naming when to prefer a different sibling (e.g. ai_classify for labeling) or any exclusions, so it is clear context without alternatives.

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