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

Annotations already cover safety (readOnly, non-destructive, openWorld, non-idempotent), and the description adds the economically critical context they cannot: $0.01 per call via x402 USDC or card key, failed calls free, and that repeated identical calls are still billed. That cost-on-repeat disclosure is exactly the kind of behavioral fact that changes agent strategy.

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?

Model choice, cost and usage guidance are front-loaded in a compact block, and almost every clause earns its place. Minor bloat from the redundant 'Same call as /ai/chat with a stronger model' repetition and a stray double period in the example.

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?

With no output schema, the description covers models, limits, price and the read-only nature of the operation, which is what an agent needs before calling. It stops short of describing the response shape (e.g. whether raw text or a JSON envelope with usage/cost is returned), a small gap for a text-generation endpoint.

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 all five parameters are already documented in structured fields. The description restates the optional system prompt, the 1,024 output-token ceiling and adjustable temperature, and adds only the useful nugget that llama-3.3-70b is the default model. That meets the baseline but does not meaningfully exceed the schema.

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 opens with a concrete verb and resource (send a prompt, get an answer) and names the exact backing models including the default. It explicitly positions itself against the sibling ai_chat as the same call with a stronger model, so an agent can distinguish the two without opening either schema.

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 states the selection condition clearly: use when the 8B model is not enough, for reasoning, drafting and Q&A, and names ai_chat as the alternative for lighter work. A worked example (prompt=Explain x402 in one sentence) anchors the expected input shape.

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