spss_factor

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

With no annotations provided, the description carries the burden of disclosing behavioral traits. It mentions a key prerequisite (IBM SPSS Statistics installed) and the core outputs (eigenvalues, variance explained, rotated factor matrix). However, it does not disclose side effects, read-only behavior, error handling, or output format details, leaving significant 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 three sentences, front-loaded with the core action, and each sentence adds distinct information: what it runs, what it produces, and a critical prerequisite. No unnecessary words or repetition.

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 complex statistical tool with five parameters and an output schema, the description is quite sparse. It fails to explain parameter meanings, extraction methods, rotation choices, or how to interpret results. While the output schema may cover return structure, the description alone is insufficient for an agent to use this tool confidently.

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 implicitly references the method parameter by naming PC and PA, and rotation via 'rotated factor matrix,' but it does not explain the required parameters file_path and variables, nor n_factors. This leaves most parameters under-specified, providing only minimal semantic value.

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 tool runs SPSS FACTOR analysis, specifically naming principal components and principal axis factoring, and lists key outputs. This unambiguously identifies the tool's function and distinguishes it from siblings like regression or cluster analysis.

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 usage for factor analysis but provides no explicit guidance on when to choose this tool over alternatives such as PCA or other dimension reduction methods. It neither mentions exclusions nor suggests alternative tools, so usage context is only implicit.

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