analyze_correlation

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

No annotations provided, so description carries full burden. It describes outputs but discloses no behavioral traits such as read-only nature, rate limits, authentication needs, or error handling. Payment is mentioned but not behavioral.

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 sentences efficient in stating purpose and outputs. Payment line is unconventional but informative. Could be slightly more structured, but it's concise and front-loaded.

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 (correlation matrix, avg pairwise correlation, diversification ratio, asset-class pair correlations). It covers core outputs and the payment caveat. Could clarify how data is fetched (schema covers this), but sufficient for basic understanding.

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 description coverage is 83%, with detailed parameter descriptions in the schema. The description adds no new parameter meaning beyond summarizing outputs. Baseline of 3 applies as schema does most of the work.

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?

Description clearly states it returns a correlation matrix and diversification ratio, specifying exact outputs (N×N matrix, avg pairwise correlation, DR>1 benefit, asset-class pair correlations). Distinguishes from sibling tools like analyze_diversification by focusing on correlation-specific metrics.

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 guidance on when to use vs. alternatives like analyze_factors or analyze_compare. Does not mention prerequisites or scenarios where this tool is preferred. The payment mention hints at cost but does not guide selection.

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