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

Annotations only declare readOnlyHint/openWorldHint, so the description correctly carries the behavioral load and delivers: the server is stateless and keeps no history, calls are metered and deduct points proportional to tokens, and the rate is given as [token cost × FX × 1.4, rounded up, minimum 5P from 2026-11-06]. That cost/stateless disclosure is real value beyond annotations; it stops short of describing failure modes, quota ceilings, or latency expectations.

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?

Purpose and model-discovery pointer are front-loaded, then scope (stateless) then cost. The bracketed pricing formula and the billing date are somewhat noisy detail for a tool description, but each sentence still maps to a decision the caller must make.

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?

There is no output schema, and the description partially compensates by naming the response field to reuse (compacted_messages). Given 8 parameters, one nested object, and a stateless contract, this is nearly complete — only the fuller response shape and error/refund behavior on failed calls are unstated.

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 every parameter is already documented in the schema, which sets the baseline at 3. The description reinforces only the messages/compacted_messages loop; it adds no syntax or format detail (e.g., content-vs-messages precedence is already in 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?

States a specific verb and resource ('Send a chat request to a selected LLM model and receive the assistant reply'), and explicitly differentiates from the sibling llm_models, which only lists models. An agent can tell immediately this is the invocation tool, not the discovery tool.

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?

Points to the sibling that must be called first ('사용 가능한 모델은 llm_models Tool로 조회합니다') and prescribes the multi-turn workflow (resend full history each call, reuse compacted_messages). It lacks explicit when-not-to-use guidance (e.g., use text_summary/text_polish for pure reformatting tasks instead of a paid chat call).

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