get_residual_correlation

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

readOnlyHint annotation covers the safety profile, and the description adds interpretive context about what the matrix represents and how to interpret it. No side effects or hidden behaviors are relevant, but the added semantic detail is valuable beyond the annotation.

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?

Three sentences, each earning its place: states the operation, explains the interpretation, and highlights an implication. Cleanly structured and front-loaded with the primary action.

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?

Given the tool's simplicity (one parameter, output schema present, read-only annotation), the description fully covers purpose and interpretation. It doesn't specify prerequisites like model group existence, but that is a minor gap for such a focused tool.

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 schema already provides 100% coverage with a clear description for model_group_id ('UUID of the model group'). The description adds no additional parameter-level meaning, so the baseline score applies.

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 the action (Get) and the specific resource (cross-asset residual correlation matrix for a model group). It distinguishes this from sibling tools like compute_correlations by emphasizing residual co-movement after factor exposures.

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 provides useful context on when the output is meaningful (high residual correlations suggest missing factors) but does not explicitly compare to alternatives or state when not to use it. Usage is implied rather than directly guided.

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