LPR利率数据

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

No annotations are provided, so the description carries the full disclosure burden. It only states the data content and does not reveal the return format, data source, update frequency, ordering of results, or how the limit parameter affects output. The verb 获取 weakly signals a read operation, but that is the extent of behavioral disclosure.

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 focused sentence that front-loads the verb and resource with no wasted words. It is appropriately sized for a one-parameter data-fetching tool, though it could have incorporated structured context (e.g., data source or output shape) without becoming verbose.

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?

The tool is low complexity (one optional parameter, no output schema, no annotations), and the description adequately conveys the data subject matter. However, because there is no output schema, the description should have indicated what the response looks like (time series vs. latest values) and the data coverage/source, which are notable gaps.

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% since the single limit parameter includes a description (返回数量). Per the baseline rule for high coverage, the description does not need to add parameter detail, and it indeed adds no parameter-level meaning beyond what the schema already documents.

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 (获取/get) with a specific resource (中国贷款市场报价利率LPR数据) and enumerates the exact tenors covered (1年期和5年期以上). This clearly differentiates the tool from the many sibling macro_* tools in the list, such as macro_money_supply, macro_gdp, and bond_yields, without ambiguity.

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 the description — an agent can infer to use this tool when LPR data is needed. However, there is no explicit statement about when to choose it over alternatives like bond_yields or macro_money_supply, nor any exclusion criteria or named sibling comparison.

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