plot_scatter

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

With no annotations, the description carries the full burden. It does not disclose whether the plot is displayed, saved to the save_path, or if there are any side effects. It also doesn't address handling of non-numeric inputs beyond stating that columns should be numeric. This is a minimal disclosure of behavior.

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, front-loaded with the core purpose, and includes a helpful example without fluff. Every sentence earns its place, making it efficient and well-structured.

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 covers the core use case adequately, but with no annotations or output schema, and 5 parameters, it lacks details on how df_name and save_path function, and what the tool returns (e.g., a plot object or file). For a plotting tool, this leaves notable gaps.

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 description adds meaning for x, y, and hue (numeric and categorical roles) and includes a concrete example. However, it leaves df_name and save_path unexplained, and schema coverage is 0%. Since more than half the parameters are undocumented, the description only partially compensates.

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 creates a scatter plot between two numeric columns, which is a specific verb+resource. It distinguishes from sibling plotting tools by focusing on scatter plots and optional hue grouping. The example further clarifies usage.

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 clear context: use to explore relationships between two numeric variables, and hue for subgroup patterns. However, it does not explicitly mention alternatives or when not to use it, though the context implies it.

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