statistica_cluster

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

With no annotations, the description carries the full burden. It helpfully discloses the outputs (cluster membership, amalgamation schedule, descriptive statistics), but says nothing about permissions, whether it mutates the spreadsheet, whether a running instance is required, or 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?

Two tight sentences, front-loaded with the operation and followed by inputs and returns. Zero 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?

For a statistical analysis tool with no annotations, no output schema, and half the parameters undocumented, the description covers purpose and outputs but omits input meaning (path/sheet), prerequisites, and any behavioral caveats.

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 coverage is only 50%, so the description should compensate. It merely restates the schema's own wording for `variables` ('the variables to cluster') and says nothing about `path` or `sheet`, both of which remain entirely undocumented.

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?

States a specific verb+resource: 'Hierarchical cluster analysis', which clearly separates it from sibling analyses like statistica_factor, statistica_anova and statistica_regression. The module number adds precision. It does not explicitly name an alternative sibling, but the resource is unambiguous.

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 when-to-use guidance, no exclusions, and no routing to or away from sibling analysis tools (e.g., factor vs cluster). The agent must infer the context 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.