get_datastructure_varset

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

readOnlyHint=true already tells the agent this is a safe read, so the description's main added value is the varset name-resolution behavior: slugs or display names, case/space/hyphen insensitive. It adds nothing about response size, truncation, or error behavior when a name doesn't resolve, which leaves a gap beyond the annotation coverage.

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 purpose, then name-resolution rules, then a compact end-to-end example — each element earns its place. The parenthetical examples are slightly verbose but serve a concrete disambiguation purpose.

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?

With 5 parameters (4 required, all schema-documented) and no output schema, the description covers the essential call shape plus the one behavioral quirk (naming) that would otherwise cause a failed lookup. It doesn't address what a returned varset contains beyond 'variables with descriptions', but that is a minor gap for a read-only drill-down tool.

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% (baseline 3), and the description still adds real meaning: it explains that varset accepts either a slug or a display name and that matching ignores case, spaces and hyphens — a detail the schema does not state. The worked example (standard='adamig', version='latest', datastructure='ADSL', varset='PopulationIndicator') further disambiguates how the four required params combine.

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 opens with a specific verb+resource ('获取单个变量集的内容'), and notes it also returns each variable's description, which distinguishes it from the listing sibling get_datastructure_varsets. It stops short of naming that sibling explicitly, but the scope line ('适合按集下钻而不要拉整包') makes the boundary clear enough to choose correctly.

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?

It gives a clear usage context — drill down per varset rather than pulling a whole package — which implicitly routes to the listing tool when the set name is unknown. The schema also tells the agent to call get_datastructure_varsets first for the applicable list, though the description itself doesn't repeat that prerequisite or state explicit exclusions.

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