inspect_response_data

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

With no annotations, the description carries the burden and does disclose meaningful behavioral scope: it audits shape, missingness, categories, ranges, and zero-variance items. However, it does not describe return behavior, whether it is read-only, or how it handles malformed or unexpected data.

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?

A single front-loaded sentence communicates the tool's purpose and audit dimensions with no wasted words. It is appropriately concise and scannable.

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?

An output schema exists, so return values need not be described, but the definition still lacks usage context relative to the sibling tools and leaves the optional item_names parameter unexplained. For a tool intended to guide when to run deeper psychometric analyses, more contextual guidance is needed.

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, but it does not explain the meaning or usage of the single required 'data' parameter beyond the schema's structural definition. It also omits the optional 'item_names' field, making the parameter semantics incomplete.

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 uses a specific verb ('Audit') and clearly identifies the resource (response data) and the dimensions it covers (shape, missingness, categories, ranges, zero-variance). This distinguishes it from the analysis-oriented siblings, though it does not explicitly name another tool or contrast itself with descriptive_statistics.

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 this is a data-quality inspection/pre-analysis step but provides no explicit guidance on when to use it versus descriptive_statistics, ctt_item_analysis, or the other sibling tools. There are no stated exclusions or routing conditions.

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