Answer a customer question

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

With no annotations, the description carries the full burden. It does disclose useful behavioral detail beyond structured fields: the intent taxonomy (faq, order_status, appointment, human_handoff) and the matched=false signal when no FAQ fits, which implies an escalation path. However, it never says whether the generated reply is sent to the customer or merely returned for review, nor what permissions or side effects apply — a material ambiguity for a customer-facing tool.

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?

Two sentences, front-loaded with the action and data sources, then the return contract. Every clause earns its place, and the return-shape detail is warranted because no output schema exists.

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?

Because there is no output schema and no annotations, the description does the heavy lifting well by enumerating the intent values and the matched=false fallback. The remaining gap is whether the reply is dispatched or returned, which leaves the agent unsure about side effects, but overall the description is sufficient to call the tool correctly.

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?

Only one parameter exists and schema coverage is 100%, so the schema already documents 'message' fully. The description adds only the language hint (English or Chinese) relevant to the message, which is marginal value. Baseline 3 applies.

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 (answer) and resource (a customer's message) and names the knowledge sources used (FAQs, order data), which distinguishes it from the booking-oriented siblings list_open_times and book_appointment without naming them. It stops short of an explicit 'not for booking' statement, so differentiation is inferential rather than stated.

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?

Usage is implied by 'answer a customer's message', and the parenthetical '(English or Chinese)' hints at the input language scope. There is no explicit when-to-use or when-not guidance, and no sibling alternatives are named, so the agent must infer the routing.

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