plot_stacked_bar

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

With no annotations, description carries full burden. It discloses normalize's effect on output (percentages vs. counts), but doesn't mention plot display/save behavior or consequences of parameters like save_path. Some behavioral context is present but not comprehensive.

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?

Three sentences including an example – concise, front-loaded with the core purpose, no unnecessary words. Each element adds value.

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?

Covers the core plot type and normalize behavior, but with 6 parameters, no schema descriptions, and no annotations, it should explain top_n, df_name, and save_path. The example helps but doesn't make the description complete.

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 has zero description coverage, so description must compensate. It explains 'column' and 'by' via the example and 'normalize' explicitly, but leaves top_n, df_name, and save_path undocumented, leaving significant gaps.

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?

Clearly states it's a 'stacked bar chart showing composition of one categorical within another', which is a specific visual type distinct from siblings like plot_bar. Includes an explicit example demonstrating 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?

Provides clear context for composition analysis (one categorical within another) and explains normalize behavior. However, it doesn't explicitly name alternatives or state when not to use, so it lacks explicit exclusions.

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