get_stock_correlation

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering safety. The description adds that it calculates Pearson correlation, but does not disclose other behavioral traits like data source, handling of missing data, or time frame specifics. The added value is modest.

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 a single sentence with no extraneous words. It is concise and front-loaded, effectively communicating the core functionality.

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?

With no output schema, the description should explain the return value (e.g., a matrix or list). It lacks this, making it incomplete for an agent to fully understand the tool's output. Given the tool's complexity, more detail is needed.

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%; both parameters have descriptions: stocks (comma-separated stock codes) and days (default 60). The description adds no additional meaning beyond the schema, so the baseline of 3 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 explicitly states the tool calculates a correlation matrix using Pearson correlation for multiple stocks. The verb '計算' (calculate) and resource '相關性矩陣' (correlation matrix) clearly define the action and output, and it distinguishes from siblings like get_stock_beta (beta) or compare_stocks (comparison).

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 implies usage for computing correlation matrices but does not explicitly state when to use this tool versus alternatives such as get_stock_beta or compare_stocks. No context on exclusions or prerequisite conditions is provided.

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