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plot_static_correlation_heatmap

Generate a publication-ready correlation heatmap from a CSV file. Specify Pearson or Spearman correlation and optionally filter columns to analyze numeric relationships.

Instructions

Generates a publication-ready Seaborn correlation heatmap. Use this when the user explicitly asks for static or publication figures. method must be 'pearson' or 'spearman'. column_filter: optional comma-separated column names or suffix patterns (e.g. '_mean') to restrict the heatmap to a subset of columns. Leave empty for all numeric columns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
methodNopearson
column_filterNo
data_file_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries full burden. It does not disclose what happens after generation (e.g., saves file, returns path), error handling, or prerequisites like data file existence. This is a significant gap for a plot tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with no redundancy. It front-loads the purpose, then provides usage guidance, then parameter details. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists, return values need not be explained. However, the description lacks preconditions (valid data file, numeric columns) and error cases. It is adequate but not thorough for a tool with 4 parameters and no annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so description must compensate. It explains method ('must be pearson or spearman') and column_filter ('comma-separated column names or suffix patterns'). But it omits explanation for required parameters data_file_path and title, which are left to the agent to infer.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it generates a publication-ready Seaborn correlation heatmap. The verb 'generates' and resource 'correlation heatmap' are specific. The sibling tool plot_interactive_correlation_heatmap indicates distinction for interactive vs static.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use this when the user explicitly asks for static or publication figures.' This provides clear when-to-use guidance. However, it does not explicitly mention alternatives (e.g., plot_interactive_correlation_heatmap) or when not to use.

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

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