plot_missing_values

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

No annotations are present, so the description carries the full burden. It discloses the heatmap's color mapping and diagnostic intent. However, it omits behavior around the optional df_name and save_path parameters, and does not state whether the plot is displayed or saved to a file, leaving operational 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 compact and front-loaded, with each sentence serving a purpose: purpose, color legend, usage condition, interpretation, and an example. There is no fluff or redundancy.

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 sufficient for selecting the tool and understanding its output, especially with the zero-argument example. However, without an output schema or annotations, the lack of parameter behavior and display/save details leaves gaps for full invocation confidence in non-default scenarios.

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?

Both parameters (df_name, save_path) have no schema description, and the description adds no parameter semantics. The example plot_missing_values() implies zero-arg invocation, but it does not explain that df_name references a loaded dataframe or that save_path controls an output file. This fails to compensate for 0% schema coverage.

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 opens with a specific verb and resource: 'Heatmap showing missing value patterns across all columns.' It also provides the color legend (white = present, colored = missing), which clearly distinguishes this tool from sibling plotting tools like plot_heatmap by focusing on missingness.

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 explicitly states when to use the tool: 'Use when quality_report shows significant missingness.' It also explains the interpretive value of the heatmap for detecting random vs. systematic missingness. It does not mention alternatives/exclusions, but the context is clear.

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