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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the content scope (the self-check checklist for the standard) but does not disclose additional runtime behavior such as return size or format, though an output schema exists to fill that gap.

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?

The description is a single sentence with no filler, front-loading the exact resource and immediately stating its practical purpose. Every word earns its place.

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?

Given that the tool has no parameters, annotations cover read-only/idempotent behavior, and an output schema exists, the description provides everything an agent needs to select and 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?

There are zero parameters, so no parameter documentation is needed. Per the rubric, a zero-parameter tool gets a baseline of 4, and the description adds relevant context about what is being retrieved.

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 uses a specific verb ('取') and a precise resource ('GB/T 47746-2026 的自查清单'), and clarifies the intended purpose: enterprises checking whether their own AI customer service meets the standard. This clearly distinguishes it from sibling tools like get_answer, list_questions, and search_answers.

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?

The description explicitly states the use case: an enterprise checking its own AI customer-service compliance against the named standard. It does not name alternatives or exclusions, but the context is clear enough for an agent to decide when to invoke this zero-parameter retrieval tool.

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