label_encode

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

No annotations are provided, so the description carries the full burden. It discloses the mapping behavior (unique values to integers starting from 0) but does not mention side effects such as whether the dataframe is modified in place, how missing values are handled, or whether new columns are created. The output schema exists but is not shown; still, key behavioral traits remain unspecified.

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: a clear purpose statement, explicit usage guidance with alternatives, and a practical example. Every sentence adds value, and the most critical information (what and when) is front-loaded.

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?

Given the tool's complexity and lack of annotations, the description is mostly complete: it explains the operation, provides usage constraints, and gives an example. However, it omits details about the 'df_name' parameter and mutation behavior, which are relevant for execution. The existence of an output schema partially mitigates the need to explain return values, but operational ambiguities remain.

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?

The input schema has 0% description coverage, so the description must compensate. It explains the 'columns' parameter via example ('columns=["City","Category"]') but does not explain 'df_name' at all. This leaves one of the two parameters undocumented in both schema and description, creating a gap.

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's function: 'Label-encode categorical columns: map each unique value to an integer (0, 1, 2, ...)'. It uses a specific verb ('map') and resource ('categorical columns'), and distinguishes itself from sibling tools by explicitly naming alternatives (one_hot_encode, target_encode) for nominal data.

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 provides explicit when-to-use guidance: 'Use ONLY for ordinal data with natural order (e.g., low/medium/high, small/large)'. It also gives a clear exclusion and alternatives: 'For nominal categories without order, use one_hot_encode or target_encode'.

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