plot_heatmap

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

With no annotations, the description must carry the full burden of behavioral disclosure. It mentions aggregation and the pivot table format, which gives some insight. However, it does not describe side effects such as whether the plot is displayed or saved (save_path parameter), nor does it mention requirements like numeric values for the metric or handling of missing data. This is a noticeable gap for a tool with no annotation support.

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 three sentences long, front-loaded with the core definition, followed by a clarifying elaboration and a concrete example. Every sentence earns its place with no redundancy or filler.

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?

Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description is fairly complete. It explains the main purpose, provides a worked example, and differentiates from siblings. However, it lacks details about df_name and save_path behavior, and does not state the expected return/output (e.g., display or save), which would be useful for an agent.

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 description coverage is 0%, so the description must compensate. It provides an example that clearly explains the core parameters: index_col and columns_col as the two categorical dimensions, values_col as the metric, and agg_func as the aggregation function. This covers most of the important parameters, though df_name and save_path are left unexplained.

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 what the tool does: it creates a heatmap of aggregated values in a pivot table format, showing a color-coded matrix of one metric across two categorical dimensions. This specific verb+resource (plot heatmap) and the explicit illustration with an example distinguish it from sibling plotting tools like plot_scatter and plot_bar.

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 clear usage context: use this when you want to visualize aggregated values across two categorical dimensions. The example demonstrates exactly how to call the tool. However, it does not explicitly state when not to use it or mention alternatives, 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.