correlation_analysis

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It merely names the analysis type and methods; it does not say whether the operation is read-only, what it returns, how missing values are handled, or whether any filtering or thresholding is applied. The word 'analysis' weakly implies computation but not enough to be transparent.

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 one short fragment with no wasted words, but it is under-specified rather than appropriately concise. For a tool with four parameters and no annotation support, this brevity sacrifices necessary information and cannot be considered well-structured for agent consumption.

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?

Even though an output schema exists, the description lacks annotations, parameter semantics, and usage guidance. An agent cannot tell what table_name refers to, what format columns expects, or what min_strength controls. The method options provide only a sliver of the context needed 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?

Schema description coverage is 0%, so the description should compensate for undocumented parameters. It does not: it only mentions Pearson/Spearman, which merely duplicates the existing method enum in the schema. There is no explanation of table_name, columns, or min_strength, leaving the agent without enough context to correctly fill three of the four 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 Russian description translates to 'Correlation analysis (Pearson/Spearman)', which identifies the domain and supported methods but never states an explicit verb, operating resource, or result. It is less a tautology than a vague purpose statement: an agent can infer the tool computes correlations, but not that it correlates columns of a table or how it differs from statistical_test.

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 gives no guidance on when to use this tool instead of alternatives like statistical_test, distribution_analysis, or cross_tab. No context, preconditions, exclusions, or alternative tool names are mentioned, so the agent must rely entirely on the tool's name and sibling list.

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