spss_value_labels

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

With no annotations, the description carries the full behavioral burden. It does disclose the command format, the expected value_labels structure, and the SPSS dependency. However, it does not state whether the original file is modified when output_path is null, whether existing value labels are overwritten, or what happens on invalid values.

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 compact and front-loaded: action, syntax, example, and dependency are each expressed in the minimum number of words. Every sentence adds information and there is no filler.

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 3-parameter tool with no annotations, the description is adequate for invoking the tool but leaves a meaningful gap around output_path behavior and file mutation semantics. It provides a concrete example and names the required dependency, but an agent cannot fully predict whether the tool edits in place or writes a new file.

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 0%, so the description must compensate. It does explain value_labels thoroughly with the nested variable->value->label example, but file_path and output_path are left entirely to name-based inference. This is partial compensation only.

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 clear imperative 'Set value labels' and immediately shows the SPSS VALUE LABELS syntax plus an example dictionary, so an agent can tell what the tool does. It does not explicitly distinguish itself from the sibling spss_variable_labels, though the command syntax and value-level example make the distinction inferable.

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?

There is no guidance about when to prefer this tool over alternatives such as spss_variable_labels, spss_formats, or spss_recode. The only usage-related context is 'Requires IBM SPSS Statistics', which is a prerequisite rather than a selection rule.

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