reliability

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

With no annotations provided, the description carries the behavioral burden, and it does well by stating what it computes and that it 'flags items that weaken the scale.' This gives an agent a realistic picture of the output behavior. It does not discuss side effects or missing-data handling, but the described behavior is read-only statistical computation and is not misleading.

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 with no filler. The first sentence is front-loaded with the core purpose and full output list, and the second adds essential reverse-scoring guidance. Every clause contributes useful information.

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 statistical tool with six parameters and no output schema, the description covers the important operational details: what it computes, what statistics are returned, and how to handle reverse-worded items. It is missing explicit preconditions such as requiring at least two numeric items or how missing values are handled, but the core calling context is well covered.

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 schema already documents all parameters. The description adds meaningful semantics by explaining that `reverse` is for reverse-worded items and must be paired with `scale_max`, which is not obvious from the schema alone. This goes beyond the baseline for fully covered schemas.

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 the specific analysis ('internal-consistency reliability for a set of scale items') and gives a concrete list of outputs: Cronbach's alpha with CI, McDonald's omega, average inter-item correlation, and per-item statistics. This clearly distinguishes it from sibling tools like correlation, factor_analysis, or test_ttest, even without naming them explicitly.

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 establishes a clear context: use when you have a set of scale items and need internal-consistency reliability. It also provides direct guidance on how to handle reverse-worded items ('Use reverse (with scale_max)'), which is an actionable usage rule. It does not explicitly mention when not to use the tool or point to alternatives, so it stops short of a 5.

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