plot_qq

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

With no annotations, the description must fully disclose behavior. It explains how to interpret the Q-Q plot ('Points on the diagonal = normal. Deviations show skewness or heavy tails'), which is useful. However, it does not disclose what the tool actually outputs (e.g., renders a plot, returns an object, saves to a path), nor does it mention side effects or requirements like numeric data.

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 highly concise and well-structured. It opens with the purpose, then explains interpretation, gives a usage tip, and ends with a concrete example. Every sentence adds value and none are wasted.

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 simple plotting tool, the description covers the main purpose and interpretation but is incomplete regarding parameters and return behavior. Since there is no output schema, the description should explain whether the plot is displayed, saved, or returned as an object. The missing df_name and save_path semantics also reduce completeness.

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 schema has 0% description coverage, so the description must compensate. It only minimally addresses the 'column' parameter through an example (plot_qq(column="Revenue")), and completely ignores 'df_name' and 'save_path'. Users have no idea what these parameters do, making the description insufficient for correct invocation.

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 purpose: 'Q-Q plot to visually assess if a column follows a normal distribution.' It identifies the resource (a column) and the specific goal (normality assessment). It also distinguishes itself from the statistical test normality_test, but does not use an explicit verb like 'creates' or 'displays', which prevents a 5.

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 usage guidance: 'Use alongside normality_test for visual confirmation.' This tells the user when to use it (in conjunction with a normality test for visual backup). It does not mention alternatives or when not to use it, but the pairing advice is clear and valuable.

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