plot_correlation_matrix

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

Discloses several behavioral traits: lower triangle mask, method options, and the meaning of empty columns (all numeric). It also implies a prerequisite. Lacks details on save_path side effects or return value, but with no annotations, it covers the key behaviors.

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 with no fluff. Each sentence adds value: what it does, method options, column semantics, usage timing, purpose, and an example.

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?

Core functionality and usage context are clear, but two parameters are left undocumented and there is no mention of how it compares to related tools like get_correlation or correlation_filter. It is adequate for a typical plotting task but not 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?

With 0% schema description coverage, the description must compensate. It explains method options and columns default, but df_name and save_path are completely unmentioned. This is only partial compensation for 4 parameters.

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 correlation heatmap with a lower triangle mask and annotations, which is a specific verb+resource. It distinguishes itself from sibling plotting tools by specifying the correlation focus and the mask/annotation behavior.

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 on when to use it ('Run after all numeric features are prepared') and what it identifies (multicollinearity and target correlations). However, it does not explicitly mention alternatives or exclusions, though the purpose is distinct enough.

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