overparameterization_analysis

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

With no annotations, the description carries the full burden and does well by disclosing the heatmap output, the highlighting of the ARMA/mu block, and the distinction between structural and true redundancy. However, it does not explicitly state whether the tool modifies any files or is strictly read-only, though 'check' and 'shown' imply it.

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 well-structured with a summary, behavioral details, usage instructions, and parameter list. Every sentence adds value, and the length is appropriate for the tool's complexity without redundancy.

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 description covers the workflow (when to run), the behavioral output (heatmap and list), and interpretation (structural vs. true redundancy). However, without an output schema, it does not specify the exact programmatic return structure, though it gives enough for a user to understand what to expect.

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?

The input schema provides zero description coverage, but the description compensates fully with a dedicated Parameters section explaining the .inp/.pre file path and the threshold default of 0.7. Both parameters are clearly documented, exceeding the schema's bare type information.

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?

Clearly states the tool checks for overparameterization by inspecting the parameter correlation matrix. The verb 'check' and the resource 'parameter correlation matrix' are specific, and the detailed description of the heatmap and flagged pairs distinguishes it from sibling diagnostic tools.

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?

Explicitly instructs when to run the tool: after estimate_and_diagnose if the diagnosis mentions 'sobreparametrización', or as a routine check before finalizing the model. This gives clear timing and even a conditional trigger, satisfying the dimension.

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