rank_target_correlations

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

No annotations are provided, so the description carries the full burden. It states that it calculates correlations for numeric columns, but does not disclose handling of missing values, data type requirements, or the fact that the method parameter defaults to 'pearson'. With an output schema present, return format is covered, but behavioral details are sparse.

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?

Two sentences with no filler. The first sentence states the core action and scope, the second gives the intended use case. Information is front-loaded and every sentence earns its place.

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 has 3 parameters (none described), no annotations, and an output schema, the description is incomplete. It fails to explain the parameters, the method options, or error conditions like non-numeric target columns. The use case is clear, but critical operational details are missing.

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%, meaning the input schema provides no parameter descriptions. The description does not mention any of the three parameters (data_file_path, target_col, method), leaving the agent to infer their roles. This is a significant 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?

Description clearly states the verb (calculates and ranks) and resource (correlation between target column and all other numeric columns). It differentiates by saying 'at once' and provides an example use case (find top features related to diagnosis), which helps distinguish from siblings like run_correlation.

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?

Description explicitly says 'Use this tool when the user wants to rank, sort, or find top features related to a specific outcome column.' This provides clear context for when to use it, though it lacks explicit 'when not to use' or alternative tools.

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