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

Annotations declare readOnlyHint=false and destructiveHint=true, and the write path ('set which column is the answer') is consistent with a mutation, so no contradiction. However, the description never says what the setting overwrites, whether the model must be retrained afterwards, or that changes are destructive — precisely the context that matters for a destructive tool with full annotation coverage already in hand.

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 read mode is front-loaded, which is good, but the whole definition is one dense, semicolon-chained sentence that packs three modes of behavior together and is hard to parse on first read.

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 five-parameter, dual-mode tool with no output schema, the description covers both modes but omits what the inspection returns (beyond 'how strongly each relates'), any pagination or size limits, and the side effects of committing column choices. Adequate for orientation, insufficient for confident invocation.

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 100%, so the schema already carries enum semantics for test_split and the role of every parameter. The description adds only the pairing rule (category_column with input_columns) and the read-vs-write framing, which the schema substantially duplicates; the baseline 3 applies.

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 resource (columns of the training file) and two distinct verbs — inspecting column/category relationships and setting the answer/input columns. It is identifiable without opening the schema, though it uses no sibling names to differentiate from dataset_summary or sample_rows.

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 implicitly routes usage by argument shape: calling with category_column and input_columns is the configuration path, anything else is the inspection path. There is no explicit when-to-use or when-not-to-use guidance relative to the many sibling tools that also touch columns and data.

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