Drop Missing

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

No annotations are supplied, so the description carries the full burden. It does disclose the return payload (row and column counts before and after), which is genuinely useful for verifying the effect of a destructive operation, but it never states whether the dataset is modified in place or a new dataset is produced, and it does not warn that removed data cannot be recovered.

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 core action is front-loaded in the first sentence and the Args/Returns block is compact with no redundant prose. The pseudo-docstring formatting is slightly verbose for two parameters but every line carries information.

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 two-parameter mutation tool with no annotations and no output schema, the definition covers both inputs and the return shape, which is most of what an agent needs. The remaining gap is the mutation model (in-place versus returned copy) and edge-case behavior when an entire row/column set is empty.

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, and it largely does: dataset_id is identified as the dataset identifier and axis is documented with both allowed values, their exact semantics, and the 'rows' default. Only the string-typing and enum constraint of axis remain implicit.

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 opens with a specific verb+resource: 'Remove rows or columns containing missing values.' It clearly conveys that this is a deletion-based missing-value handler, which implicitly separates it from the fill_missing_* and impute_* siblings, but it never names an alternative, so an agent must infer the distinction from the sibling list.

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?

Usage is only implied: the axis semantics tell the agent what each mode does, but there is no statement of when to prefer dropping over imputing (e.g. when missingness is rare or rows are otherwise unusable), nor any prerequisite about having loaded a dataset. The reader must derive the use case themselves.

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