get_datastructure_varsets

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

readOnlyHint=true already covers the safety profile. The description adds the behavioral note that names are unstable across versions, but does not describe the result shape beyond the examples, nor whether the list is paginated or how raw=true affects the payload (that is left to the schema).

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 purpose, then a workflow caveat, then a compact example invocation. Three short lines with no filler, though the example line could arguably be redundant given 100% schema coverage.

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?

For a read-only lookup with full schema coverage and no output schema, it covers what is needed: verb, target resource, key fields of the response, and a worked example. It falls short of 5 because it does not clarify the distinct roles of get_datastructure_varset (singular) vs this list 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%, so the baseline is 3. The description adds real value by prescribing an example combination and warning that names vary by version, reinforcing the need for the correct 'version' and 'datastructure'. The nested 'datastructure' schema already points to get_datastructures, so the description adds only modest extra context.

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 names a specific verb (获取) and resource (变量集清单 for an analysis datastructure), with examples of varset kinds (Identifier / Treatment / Trial Experience). It is distinguishable from siblings like get_datastructure_variables and get_datastructure, though it does not explicitly contrast them in words.

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 states a usage rule: var-noiset names are unstable across versions, so come here first to get the version-specific available names before drilling down. That is implied guidance for a workflow, but there is no explicit when-not guidance and no mention of the sibling naming ambiguity (varset vs variables).

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