Boxplot

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

With no annotations supplied, the description carries the full burden, and it does disclose the key side effect (images are saved) plus the return shape (dataset ID, selected columns, filename). It omits file format, output location, overwrite behavior, and what happens when a non-numerical column is passed — meaningful gaps for a tool that writes artifacts.

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?

Front-loaded with the action, then a compact Args/Returns block with zero filler. Every line conveys information an agent would otherwise have to guess.

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 plotting tool, the definition covers purpose, the optional-parameter branch, and the return contents, which is sufficient even without an output schema or annotations. Only the persistence details of the saved image are left unaddressed.

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 largely does: it documents both parameters, marks column_name as optional, and explains the null default's effect. It adds little on dataset_id beyond 'ID of the dataset', leaving format/validation ambiguous.

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-and-resource pair ('Generate and save box plots') and states its scope ('one or all numerical columns'), which is enough to distinguish it from plotting siblings such as countplot or barplot. It does not explicitly name an alternative, so it stops short of the top score.

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 explains the conditional behavior — supply column_name for a single column, omit it to plot all numerical columns — which implies the two usage modes. There is no guidance on when to prefer this over sibling plotting tools like auto_visualize or analyze_distribution, and no exclusions or prerequisites.

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