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. It discloses core behaviors: computing sum/mean, optional reverse coding, and minimum valid item count, plus the installation requirement. However, it does not describe side effects like whether it overwrites variables, or what happens when min_valid is not met, leaving notable transparency gaps.

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 two sentences long, front-loaded with the main action and key features. Every phrase adds value without redundancy, making it highly concise and well-structured.

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 provides a high-level overview but lacks details on parameter semantics, edge cases, and dataset mutation behavior. The presence of an output schema helps but does not cover the full context needed to use the tool correctly.

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 adds conceptual meaning for some parameters by mentioning method (SUM/MEAN), reverse coding, and minimum valid item count. But it does not explain individual parameters like reverse_min/reverse_max or the items array, leaving ambiguity for a tool with 8 parameters.

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 clearly states the action ('Compute a scale score'), the resource ('from multiple item variables'), and the methods ('SUM or MEAN'). It also mentions optional reverse coding and minimum valid count, which distinguishes it from sibling tools like spss_reliability_alpha that compute reliability instead.

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 gives clear context for when to use the tool: to compute scale scores from item variables. It also provides a prerequisite ('Requires IBM SPSS Statistics to be installed'). However, it does not explicitly mention when not to use it or recommend alternative tools for other scenarios.

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