Get Value Labels

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds only that the result is a label mapping used for decoding; it leaves open whether the mapping covers the whole dataset or only selected variables, and gives no cost/size context.

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?

One short sentence with no filler, and the core purpose is front-loaded. It is however extremely terse given the ambiguity about mapping scope, so it sits slightly below ideal.

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?

An output schema exists, so return-value shape needn't be explained, and annotations cover safety. The remaining gap is scope: whether labels are returned for every variable in the dataset or filtered somehow, which matters for an agent deciding whether to call this per dataset.

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%, so the single `path` parameter (relative to SAS_DATA_ROOT or absolute) is fully documented in the schema. The description adds nothing about the parameter, so the 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 and resource: it returns SAS format value-label mappings. The parenthetical adds a concrete purpose (decoding coded values during QC), which distinguishes it from generic readers like read_dataset. It does not explicitly name a sibling to differentiate from, so it stops short of 5.

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 phrase 'QC 时解码 coded 值用' implies when to reach for it (data-quality checks on coded values), which is more than nothing. However, no alternative is named (e.g., get_dataset_metadata for variable metadata) and no exclusion or prerequisite is stated.

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