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

With no annotations, the description carries the full load and does disclose the important behavioral traits: batching limits (up to 32 texts, 32,000 characters total) and a concrete price of $0.002 USDC on Base via x402, which signals that a paid x402 flow is involved before invocation. It does not explain payment failure behavior, rate limits, or what happens if the character budget is exceeded, 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 dense sentences, front-loaded with model and dimensions, then capacity limits, then use cases, then price — no filler and nothing buried. The only minor opacity is the terse "POST texts array" wording, which does not cost enough to drop below 5.

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 one-parameter, no-output-schema tool, the description covers the essentials: model, vector size, batch capacity, intended applications, and cost/payment rail. The remaining gap is error and payment-flow behavior (what happens on insufficient USDC or on exceeding the character budget), which an agent would want before a paid call.

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 100% for the single `text` parameter, so the baseline is 3, but the description adds real information the schema lacks: the 32-item batch cap and the 32,000-character aggregate limit. The phrase "POST texts array" is slightly opaque given the schema only declares a `text` property with additionalProperties=false, leaving a small ambiguity about the exact array key.

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 specific verb+resource ("Text embeddings") plus the concrete model and vector dimensionality (bge-m3, 1,024 dims, multilingual), which is exactly what an agent needs to distinguish it from siblings like ai_chat or ai_translate. There is no ambiguity about what the tool returns or which modality it handles.

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?

"For semantic search, clustering and similarity" gives clear intended use cases that let an agent decide whether embeddings are the right primitive. It stops short of explicit exclusions (e.g., "not for generation — use ai_chat"), so it is a strong 4 rather than a 5.

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