spss_compute_scale_score

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses the computation method (SUM/MEAN), optional reverse coding, and minimum valid item count, which are useful behavioral details. However, it does not disclose side effects such as whether the file is modified in place or whether a new variable is added, nor does it mention prerequisites about variable existence.

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?

Two sentences with no wasted words. The core function and key options are front-loaded, followed by the environmental requirement. Every sentence adds value.

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 tool with 8 parameters and no annotations, the description gives a solid high-level overview but omits operational details like how reverse coding determines values, how min_valid interacts with missing data, and whether the result is written to the file or returned. The presence of an output schema mitigates return-value ambiguity, but the reverse coding and file-mutation behavior remain underspecified.

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 meaningfully maps to parameters: 'SUM or MEAN' covers the method enum, 'multiple item variables' covers items, 'optional reverse coding' covers reverse_items/reverse_min/reverse_max, and 'minimum valid item count' covers min_valid. It leaves file_path and new_variable implicit, but these are self-evident from their names.

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 states a specific verb ('Compute') and resource ('scale score') with the SUM/MEAN methods and optional reverse coding and minimum valid item count. It clearly distinguishes this tool from generic spss_compute and analysis tools like spss_reliability_alpha by focusing on scale scoring from item variables.

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 description implies the use case (computing scale scores from multiple item variables) but does not explicitly state when to prefer this over siblings such as spss_compute or spss_recode, nor does it mention exclusions or alternatives. The only contextual guidance is the IBM SPSS Statistics requirement, which is a prerequisite rather than a usage comparison.

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