correlation_analysis

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

There are no annotations, so the description carries the full burden. It does disclose the operation ('Compute'), the output ('Returns correlation matrix and top ranked strongest positive/negative correlation pairs'), and the supported methods. However, it does not explicitly state that the operation is read-only or explain behavior around missing values, non-numeric columns, or sampling.

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 short and front-loaded with the main action. It loses a point for the redundancy of mentioning 'correlation matrix' twice, but it otherwise contains no filler.

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 tool is moderately complex, with four parameters and no annotation coverage, yet the description omits key contextual details such as what 'sample' does, what happens when 'columns' is null, and how the session dataset is used. The presence of an output schema reduces the need to describe return values, but the parameter-context gap remains significant.

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%, so the description must compensate. It partially explains 'method' by listing valid values and 'columns' indirectly via 'numeric columns', but 'sample' and 'session_id' are not explained at all. The description is not enough to correctly set parameters beyond method.

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 uses a specific verb ('Compute') with a clear resource ('correlation matrix between numeric columns') and lists the supported methods. This makes the tool's purpose immediately distinguishable from all sibling tools, none of which mention 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?

The description implies usage whenever a correlation matrix or strongest pairwise correlations are needed, but it does not explicitly state when to use this tool versus alternatives like compute_statistic, describe_dataset, or create_visualization. No exclusions or prerequisites are provided.

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