stats_correlation-matrix

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds valuable output details (Pearson/Spearman matrices, eigenvalues, eigenvectors, explained variance ratios) beyond annotations, justifying a score above baseline.

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?

Description is concise: two sentences cover purpose, usage, and return values. No unnecessary words, though could be more structured. Efficient for agent consumption.

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?

No output schema, so description explains return values adequately. However, it mentions 'covariance matrices' in the first sentence but the returns only list correlation matrices, creating a slight inconsistency. Otherwise sufficient for a 3-parameter 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?

Schema coverage is 100% with descriptions. The description adds context about providing a '2D array of return series,' which is similar to the schema's 'Named data series' example. Marginal added value, so baseline 3.

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 it computes correlation matrices with optional eigenvalue decomposition. Distinguishes from sibling tools like risk_correlation by mentioning eigenvalue decomposition, though not explicitly compared.

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?

Provides explicit usage context: 'Use when computing a correlation matrix with eigenvalue decomposition for multiple assets.' Does not state when not to use or mention alternatives, but the guidance is clear.

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