cross_tab

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

With no annotations, the description carries the behavioral burden. It discloses key outputs (frequencies, percentages, chi-square) but does not clarify assumptions, side effects, handling of missing values, or whether aggregation changes the chi-square computation. Some transparency is present, but significant behavioral details are missing.

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 a single, front-loaded sentence with no filler. Every word contributes meaning, and it is appropriately sized for the tool's scope.

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?

Although an output schema exists, the tool has six parameters with zero description coverage and no annotations. The description does not explain how to configure aggregation, percentage display, or value column selection, leaving the agent without enough context to invoke the tool confidently in varied scenarios.

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 only implicitly maps 'two categories' to row_column and col_column; agg_function, value_column, and show_percent are not explained. The mention of 'percentages' hints at show_percent, but overall the description adds little semantic value over the bare parameter 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 operation — cross-tabulation of two categorical fields — and names concrete outputs: frequencies, percentages, and chi-square. This clearly distinguishes it as an analytical tool, though it does not explicitly differentiate from nearby siblings like pivot_table or statistical_test.

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 phrase 'cross-tabulation of two categories' gives clear context for when the tool is appropriate: when an agent needs a contingency table for two categorical columns. It doesn't provide exclusions or name alternatives, but the intended use is reasonably clear.

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