plot_correlation_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 does state the output format (JSON + inline PNG), which is a useful behavioral trait for the agent. However, it omits other important behaviors such as whether it is read-only, how parameters affect output, or any side effects.

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 highly concise and front-loaded, stating the core purpose and output format in two short fragments. It contains no fluff or redundancy. The second fragment 'JSON + inline PNG' is elliptical but still informative, making this an efficient, appropriately sized description for the little it conveys.

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 absence of annotations and output schema, the description is under-specified. It fails to explain what the JSON contains, how to use the method and columns parameters, or what the heatmap encodes. The tool has multiple parameters and no formal documentation, so this description is insufficient for reliable invocation.

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?

The description makes no mention of the three schema parameters (source_id, method, columns). Schema description coverage is 0%, so the agent gets no semantic information about what these parameters do, the meaning of the default method, or how columns can be used. This is a severe gap.

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 identifies the tool as generating a correlation matrix heatmap, which distinguishes it from other plot types. It specifies the output format (colored heatmap) but lacks an explicit verb like 'plot' or 'generate'. The resource (correlation matrix) is specific, making the purpose readily understood.

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?

There is no guidance on when to use this tool versus alternatives such as check_correlations, check_multicollinearity, or plot_scatter. The description does not mention any context, prerequisites, or exclusions, leaving the agent without selection criteria.

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