statistica_correlation

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

No annotations are provided, so the description carries the full behavioral burden and it is essentially silent. It does not say where results go (attached sheet, new output, console), whether the process is blocking, what happens with missing data or non-numeric columns, or whether the existing STATISTICA instance state is modified. The one behavioral clue — 'attach' in the schema — is undocumented by the description itself.

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?

A single tight sentence with zero waste and the resource front-loaded. It is appropriately sized, but its brevity is achieved by omission of substance rather than by economy.

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?

For a 4-parameter analysis tool with no annotations, no output schema, and 25% schema coverage, the definition is essentially a one-line label. Nothing tells the agent how to invoke it correctly, how results are returned, or how it differs from the near-identically named sibling statistica_correlation_matrix. It is not adequate to drive correct 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 only 25% (only 'attach' is described, and that is in the schema, not the description). The description adds no meaning at all: it does not explain 'variables' selection semantics, what 'sheet' accepts, or how 'path' relates to the running instance. With three undocumented parameters, the description fails to compensate for the coverage 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 states a specific computation ('Pearson correlation matrix') and its engine ('STATISTICA Basic Statistics module'), which distinguishes it from the sibling statistica_correlation_matrix only weakly — the two names suggest near-identical purpose, and the description gives no basis for choosing between them. For an agent facing both tools, this is a real gap, but the verb+resource is still concrete.

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?

No guidance on when to use this tool versus the sibling statistica_correlation_matrix, or the broader statistica_regression/statistica_factor tools that also consume correlation matrices. No prerequisites, no mention of required data shape. The agent must infer everything from the name alone.

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