compute_correlation_matrix

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

Annotations already indicate readOnlyHint=true (safe read), idempotentHint=true, destructiveHint=false. The description adds useful behavioral context: it uses actual daily returns, detects regime changes, and returns specific data formats. This goes beyond annotations without contradiction.

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, front-loaded with the primary action and scope. No redundant information. Every sentence adds value.

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 no output schema, the description adequately explains return values (heatmap data, average pairwise correlation, risk concentration score). It also notes the regime detection feature. A full picture is provided for a compute tool with few parameters and safe annotations.

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 67% (2 of 3 parameters have descriptions). The description mentions 'list of symbols' but does not add detail for period or threshold beyond what the schema provides. Baseline 3 is appropriate since schema does the heavy lifting.

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 tool computes a cross-asset correlation matrix for a list of symbols, using actual daily returns. It distinguishes from siblings like compute_factor_exposure and compute_volatility_surface by specifying the output (heatmap data, average pairwise correlation, risk concentration score) and behavior (detects regime changes).

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 this tool vs alternatives. While the purpose is clear, there is no mention of when not to use it or which sibling tools might be more appropriate for other tasks (e.g., compute_factor_exposure for factor exposure).

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