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

Beyond the annotations, the description discloses pricing ($0.002/call), billing paths (x402 USDC or card key via /credits), that failed calls are free, that repeats cost money, that no account or provider key is needed, and that reasoning traces are stripped from the output. That is exactly the extra context annotations cannot express.

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?

Front-loaded with the model identity and call shape, then cost and access details. It is a dense run-on with a slightly awkward example fragment ("Example: prompt=Explain x402 in one sentence.."), but nearly every clause carries useful information.

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-call inference tool with no output schema, the description covers cost, auth alternatives, output limits, what the response omits (reasoning), and side-effect-free behavior. An agent has everything needed to decide to call it and predict the charge.

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%, so the schema already documents prompt length, system prompt, maxTokens range/default, and temperature range. The description adds only the 1,024 output-token cap and an example prompt, which duplicates the schema's maxTokens note without adding format or tradeoff guidance (e.g. how temperature affects this model).

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 a specific verb (chat), a specific model (Alibaba Qwen3 30B-A3B MoE, Apache 2.0), and the exchange shape (send prompt + optional system prompt, get answer). This sharply distinguishes it from siblings like ai_model_gpt_oss_120b or ai_model_mistral_small that share the same pattern.

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?

"Use when an agent wants this model by name" gives an explicit selection rule against the other ai_model_* siblings, and the OpenAI-compatible alias note clarifies an alternate route. It stops short of saying when to prefer a sibling like ai_chat_large instead, but the routing intent is clear.

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