plot_cumulative

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

With no annotations, the description explains the core output behavior (cumulative distribution) but does not disclose prerequisites (e.g., numeric column), potential side effects (e.g., displaying a figure), or details about df_name and save_path behavior. This leaves some behavioral ambiguity.

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 concise and well-structured: it defines the plot type, explains its function, states a use case, and provides an example. Every sentence earns its place, and it is appropriately sized for a simple plotting tool.

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?

The description is incomplete for operational use: it does not clarify how df_name selects the dataframe, what save_path does, or what the function returns/displays. Given the lack of schema parameter descriptions, this leaves important gaps for an agent to invoke the tool correctly.

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 example illustrates the column parameter, but df_name and save_path are not explained. Since schema description coverage is 0%, the description fails to compensate for these undocumented parameters, leaving their semantics mostly to inference from names.

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 identifies the tool as a CDF plot and explains what it shows ('percentage of data falls below each value'). This distinguishes it from other plot types like histogram or scatter, and the example reinforces the intended use.

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 provides a specific use case ('useful for understanding thresholds'), giving agents context for when to choose this tool. However, it does not mention alternatives or exclusion criteria, which would be helpful in a toolset with many plotting siblings.

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