check_dimensionality

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

No annotations exist, so the description carries the full burden. It discloses the algorithmic approach (standardizes, SVD-based PCA), output details (variance-explained curve, intrinsic dim with thresholds, Hopkins statistic interpretation), and constraints (Caps at 50 features, output size small). This is transparent but doesn't mention how non-numeric columns or missing values are handled.

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 compact and information-dense, with the key technique in the first line. The 'Level 3' tag and technical jargon might be opaque to some users, but overall it's efficient.

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 lists expected outputs and thresholds, which is useful, but lacks guidance on when to use the tool, parameter behavior, and edge cases. The presence of an output schema reduces the need to describe return types, but the input parameters remain underspecified.

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 0%, and the description does not explain the parameters source_id or columns. The mention of 'numeric matrix' and 'Caps at 50 features' indirectly relates to columns, but there's no explicit semantics for what columns accepts or its default behavior.

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 clearly states it performs PCA-based dimensionality analysis and Hopkins clustering tendency, listing specific outputs (variance-explained curve, intrinsic dim, effective rank, Hopkins statistic). This distinguishes it from sibling check_* tools which focus on other aspects.

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 explicit 'use when' instructions or alternatives are mentioned. The description implies usage for dimensionality assessment but doesn't differentiate from similar tools like check_multicollinearity or check_feature_signal. The 'Level 3' tag and 'Caps at 50 features' provide some context but not explicit guidance.

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