plot_facet

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

With no annotations, the description discloses key behavioral traits: it returns JSON plus an inline PNG, and ncols controls grid width. However, it does not describe error handling, data requirements, or whether the source must be loaded, leaving some behavioral gaps.

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?

Description is compact and front-loaded with purpose, then output format and a useful naming note. No wasted sentences, though the rename note could be considered tangential for an AI agent.

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?

No output schema and no annotations, so the description must carry more context. It fails to explain key required parameters like source_id and column, and only vaguely mentions JSON output without structure, making it insufficient for a 5-parameter tool.

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?

Only ncols gets semantic explanation (controls grid width); source_id, column, facet_by, and bins are left entirely to their names. With 0% schema coverage, the description should compensate far more for these parameters.

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?

Description clearly states it creates a distribution faceted by a categorical variable ('small multiples'), which differentiates it from sibling tools like plot_distribution and plot_boxplot. It also specifies the output format (JSON + inline PNG).

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?

Implied use case is when a distribution should be broken down by a categorical variable, but there is no explicit comparison to plot_distribution or guidance on when not to use it. The rename note clarifies ncols but not selection criteria.

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